Publications

@article{bartolo-2022-models, author = {Max Bartolo and Tristan Thrush and Sebastian Riedel and Pontus Stenetorp and Robin Jia and Douwe Kiela}, title = {Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants}, journal = {CoRR}, volume = {abs/2112.09062}, year = {2021}, url = {https://arxiv.org/abs/2112.09062}, eprinttype = {arXiv}, eprint = {2112.09062}, timestamp = {Mon, 03 Jan 2022 15:45:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-09062.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

@article{lu2021fantastically, title={Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity}, author={Lu, Yao and Bartolo, Max and Moore, Alastair and Riedel, Sebastian and Stenetorp, Pontus}, journal={arXiv preprint arXiv:2104.08786}, year={2021} }

@inproceedings{mozes-etal-2021-contrasting, title = "Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification", author = "Mozes, Maximilian and Bartolo, Max and Stenetorp, Pontus and Kleinberg, Bennett and Griffin, Lewis", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.651", pages = "8258--8270", abstract = "Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient.", }

@inproceedings{ chen2021relation, title={Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations}, author={Yihong Chen and Pasquale Minervini and Sebastian Riedel and Pontus Stenetorp}, booktitle={3rd Conference on Automated Knowledge Base Construction}, year={2021}, url={https://openreview.net/forum?id=Qa3uS3H7-Le} }

@inproceedings{ niepert2021imle, author={Mathias Niepert and Pasquale Minervini and Luca Franceschi}, title={Implicit {MLE:} Backpropagating Through Discrete Exponential Family Distributions}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS)}, year={2021}, url={https://arxiv.org/abs/2106.01798} }

@misc{bartolo2021improving, title={Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation}, author={Max Bartolo and Tristan Thrush and Robin Jia and Sebastian Riedel and Pontus Stenetorp and Douwe Kiela}, year={2021}, eprint={2104.08678}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@inproceedings{kiela-etal-2021-dynabench, title = "Dynabench: Rethinking Benchmarking in {NLP}", author = "Kiela, Douwe and Bartolo, Max and Nie, Yixin and Kaushik, Divyansh and Geiger, Atticus and Wu, Zhengxuan and Vidgen, Bertie and Prasad, Grusha and Singh, Amanpreet and Ringshia, Pratik and Ma, Zhiyi and Thrush, Tristan and Riedel, Sebastian and Waseem, Zeerak and Stenetorp, Pontus and Jia, Robin and Bansal, Mohit and Potts, Christopher and Williams, Adina", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.324", doi = "10.18653/v1/2021.naacl-main.324", pages = "4110--4124", abstract = "We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field.", } }

@inproceedings{mozes2021frequency, title={Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples}, author={Mozes, Maximilian and Stenetorp, Pontus and Kleinberg, Bennett and Griffin, Lewis}, booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}, pages={171--186}, year={2021} }

@misc{lewis2021paq, title={PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them}, author={Patrick Lewis and Yuxiang Wu and Linqing Liu and Pasquale Minervini and Heinrich Küttler and Aleksandra Piktus and Pontus Stenetorp and Sebastian Riedel}, year={2021}, eprint={2102.07033}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@inproceedings{ arakelyan2021complex, title={Complex Query Answering with Neural Link Predictors}, author={Erik Arakelyan and Daniel Daza and Pasquale Minervini and Michael Cochez}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=Mos9F9kDwkz} }

@inproceedings{lewis-etal-2020-pretrained, title = "Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art", author = "Lewis, Patrick and Ott, Myle and Du, Jingfei and Stoyanov, Veselin", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.17", pages = "146--157", abstract = "A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.", }

@inproceedings{wu-etal-2020-dont-read, title = "Don{'}t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering", author = "Wu, Yuxiang and Riedel, Sebastian and Minervini, Pasquale and Stenetorp, Pontus", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.244", pages = "3029--3039", }

@article{doi:10.1162/tacl\_a\_00338, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl_a_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } }

@inproceedings{petroni2020kilt, title={KILT: a Benchmark for Knowledge Intensive Language Tasks}, author={Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vassilis Plachouras, Tim Rockt{\"{a}}schel, Sebastian Riedel}, booktitle={arXiv:2009.02252}, year={2020} }

@misc{xiong2020answering, title={Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval}, author={Wenhan Xiong and Xiang Lorraine Li and Srini Iyer and Jingfei Du and Patrick Lewis and William Yang Wang and Yashar Mehdad and Wen-tau Yih and Sebastian Riedel and Douwe Kiela and Barlas Oğuz}, year={2020}, eprint={2009.12756}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@misc{lewis2020question, title={Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets}, author={Patrick Lewis and Pontus Stenetorp and Sebastian Riedel}, year={2020}, eprint={2008.02637}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@inproceedings{lewis-etal-2020-mlqa, title = "{MLQA}: Evaluating Cross-lingual Extractive Question Answering", author = "Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.653", doi = "10.18653/v1/2020.acl-main.653", pages = "7315--7330", abstract = "Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. We evaluate state-of-the-art cross-lingual models and machine-translation-based baselines on MLQA. In all cases, transfer results are shown to be significantly behind training-language performance.", }

@article{Lewis2020RetrievalAugmentedGF, title={Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks}, author={Patrick Lewis and Ethan Perez and Aleksandara Piktus and Fabio Petroni and Vladimir Karpukhin and Naman Goyal and Heinrich Kuttler and Mike Lewis and Wen-tau Yih and Tim Rocktaschel and Sebastian Riedel and Douwe Kiela}, journal={ArXiv}, year={2020}, volume={abs/2005.11401} }

@inproceed@inproceedings{ petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} }

@article{Karpukhin2020DensePR, title={Dense Passage Retrieval for Open-Domain Question Answering}, author={Vladimir Karpukhin and Barlas Ouguz and Sewon Min and Patrick Lewis and Ledell Yu Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih}, journal={ArXiv}, year={2020}, volume={abs/2004.04906} }

@article{Perez2020UnsupervisedQD, title={Unsupervised Question Decomposition for Question Answering}, author={Ethan Perez and Patrick Lewis and Wen-tau Yih and Kyunghyun Cho and Douwe Kiela}, journal={ArXiv}, year={2020}, volume={abs/2002.09758} }

@inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={Fabio Petroni and Tim Rocktäschel and Patrick Lewis and Anton Bakhtin and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, url="https://arxiv.org/abs/1909.01066" year = "2019", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = nov, publisher = "Association for Computational Linguistics" }

@inproceedings{lewis2019unsupervisedqa, title = "Unsupervised Question Answering by Cloze Translation author = "Lewis, Patrick and Denoyer, Ludovic and Riedel, Sebastian", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1906.04980", abstract="Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsupervised Extractive QA. We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or named entity mentions from these paragraphs as answers. Next we convert answers in context to "fill-in-the-blank" cloze questions and finally translate them into natural questions. We propose and compare various unsupervised ways to perform cloze-to-natural question translation, including training an unsupervised NMT model using non-aligned corpora of natural questions and cloze questions as well as a rule-based approach. We find that modern QA models can learn to answer human questions surprisingly well using only synthetic training data. We demonstrate that, without using the SQuAD training data at all, our approach achieves 56.4 F1 on SQuAD v1 (64.5 F1 when the answer is a Named entity mention), outperforming early supervised models." }

@inproceedings{hosking-riedel-2019-evaluating, title = "Evaluating Rewards for Question Generation Models", author = "Hosking, Tom and Riedel, Sebastian", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/N19-1237", pages = "2278--2283", abstract = "Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to optimise only the one-step-ahead prediction. However, at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process (exposure bias). A number of authors have suggested that optimising for rewards less tightly coupled to the training data might counter this mismatch. We therefore optimise directly for various objectives beyond simply replicating the ground truth questions, including a novel approach using an adversarial discriminator that seeks to generate questions that are indistinguishable from real examples. We confirm that training with policy gradient methods leads to increases in the metrics used as rewards. We perform a human evaluation, and show that although these metrics have previously been assumed to be good proxies for question quality, they are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source.", }

@article{DBLP:journals/corr/abs-1809-01494, author = {Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rockt{\"{a}}schel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel}, title = {Interpretation of Natural Language Rules in Conversational Machine Reading}, journal = {CoRR}, volume = {abs/1809.01494}, year = {2018}, url = {http://arxiv.org/abs/1809.01494}, archivePrefix = {arXiv}, eprint = {1809.01494}, timestamp = {Fri, 05 Oct 2018 11:34:52 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1809-01494}, bibsource = {dblp computer science bibliography, https://dblp.org} }

@inproceedings{spithourakis2018numeracy, month={July}, year={2018}, author={Georgios Spithourakis, Sebastian Riedel}, title={Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL)}, }

@inproceedings{weissenborn2018jack, month={July}, year={2018}, author={Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel}, title={Jack the Reader – A Machine Reading Framework}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL) System Demonstrations}, }

@inproceedings{mitchell2018extrapolation, month={July}, year={2018}, author={Jeff Mitchell, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel}, title={Extrapolation in NLP}, booktitle={Proceedings of the Generalization in the Age of Deep Learning NAACL 2018 Workshop}, }

@inproceedings{dettmers2018aaai, month={February}, year={2018}, author={Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel}, booktitle={Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI '18)}, title={Convolutional 2D Knowledge Graph Embeddings}, }

@inproceedings{kasewa18emnlp, pages={4977--4983}, year={2018}, author={Sudhanshu Kasewa, Pontus Stenetorp, Sebastian Riedel}, publisher={Association for Computational Linguistics}, title={Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, }

@inproceedings{W18-5515, pages={97--102}, year={2018}, author={Takuma Yoneda and Jeff Mitchell and Johannes Welbl and Pontus Stenetorp and Sebastian Riedel}, publisher={Association for Computational Linguistics}, title={UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)}, booktitle={Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)}, }

@article{welbl2018tacl, volume={6}, journal={Transactions of the Association for Computational Linguistics}, pages={287--302}, keywords={}, issn={2307-387X}, year={2018}, author={Johannes Welbl, Pontus Stenetorp, Sebastian Riedel}, title={Constructing Datasets for Multi-hop Reading Comprehension Across Documents}, }

@inproceedings{sanchez2018behavior, pages={1975--1985}, year={2018}, author={Ivan Sanchez, Jeff Mitchell, Sebastian Riedel}, title={Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, }

@inproceedings{minervini18conll, pages={65--74}, year={2018}, author={Pasquale Minervini, Sebastian Riedel}, publisher={Association for Computational Linguistics}, title={Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge}, booktitle={Proceedings of the 22nd Conference on Computational Natural Language Learning, CoNLL 2018}, }

@inproceedings{liepins2017summa, month={April}, year={2017}, author={Renars Liepins and Ulrich Germann and Guntis Barzdins and Alexandra Birch and Steve Renals and Susanne Weber and Peggy van der Kreeft and Herv\'e Bourlard and Jo\~ao Prieto and Ondrej Klejch and Peter Bell and Alexandros Lazaridis and Alfonso Mendes and Sebastian Riedel and Mariana S. C. Almeida and Pedro Balage and Shay B. Cohen and Tomasz Dwojak and Philip N. Garner and Andreas Giefer and Marcin Junczys-Dowmunt and Hina Imran and David Nogueira and Ahmed M. Ali and Sebasti\~ao Miranda and Andrei Popescu-Belis and Lesly Miculicich Werlen and Nikos Papasarantopoulos and Abiola Obamuyide and Clive Jones and Fahim Dalvi and Andreas Vlachos and Yang Wang and Sibo Tong and Rico Sennrich and Nikolaos Pappas and Shashi Narayan and Marco Damonte and Nadir Durrani and Sameer Khurana and Ahmed Abdelali and Hassan Sajjad and Stephan Vogel and David Sheppey and Chris Hernon and Jeff Mitchell}, booktitle={Proceedings of the EACL 2017 Software Demonstrations}, title={The SUMMA Platform Prototype}, }

@inproceedings{rocktaschel2017nips, month={December}, year={2017}, author={Rocktaschel, Tim and Riedel, Sebastian}, booktitle={Advances in Neural Information Processing Systems (NIPS)}, title={End-to-end Differentiable Proving}, }

@inproceedings{augenstein2017scienceie, month={August}, year={2017}, author={Augenstein, Isabelle and Das, Mrinal Kanti and Riedel, Sebastian and Vikraman, Lakshmi Nair and McCallum, Andrew}, title={SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications}, booktitle={Proceedings of SemEval}, }

@inproceedings{minervini2017adversarial, month={August}, year={2017}, author={Minervini, Pasquale and Demeester, Thomas and Rocktaschel, Tim and Riedel, Sebastian}, booktitle={Proceedings of the 33th Annual Conference on Uncertainty in AI (UAI)}, title={Adversarial Sets for Regularising Neural Link Predictors}, }

@inproceedings{augenstein2017multitask, month={July}, year={2017}, author={Augenstein, Isabelle and Sogaard, Anders}, title={Multi-Task Learning of Keyphrase Boundary Classification}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL)}, }

@inproceedings{bosnjak2017forth, month={August}, year={2017}, author={Bosnjak, Matko and Rocktaschel, Tim and Naradowsky, Jason and Riedel, Sebastian}, booktitle={Proceedings of the 34th International Conference on Machine Learning (ICML)}, title={Programming with a Differentiable Forth Interpreter}, }

@inproceedings{collins2017conll, year={2017}, author={Collins, Ed and Augenstein, Isabelle and Riedel, Sebastian}, booktitle={Proceedings of the 21th Conference on Computational Natural Language Learning (CoNLL)}, title={A Supervised Approach to Extractive Summarisation of Scientific Papers}, }

@inproceedings{shimaoka2017eacl, month={April}, booktitle={Proceedings of the European Chapter of the ACL (EACL)}, title={Neural Architectures for Fine-grained Entity Type Classification}, year={2017}, author={Shimaoka, Sonse and Stenetorp, Pontus and Riedel, Sebastian and Inui, Kentaro}, }

@inproceedings{catlingl2017clinical, journal={Intensive Care Society State of the Art}, title={Towards automated clinical coding in critical care}, year={2017}, author={Finn Catling, Georgios Spithourakis, Sebastian Riedel}, }

@inproceedings{trouillon17knowledge, volume={abs/1702.06879}, eprint={1702.06879}, journal={CoRR}, year={2017}, author={Th\'eo Trouillon and Christopher R. Dance and Johannes Welbl and Sebastian Riedel and \'Eric Gaussier and Guillaume Bouchard}, title={Knowledge Graph Completion via Complex Tensor Factorization}, }

@inproceedings{Sanchez2017EACL, month={April}, year={2017}, author={Sanchez, Ivan and Riedel, Sebastian}, title={How Well Can We Predict Hypernyms from Word Embeddings? A dataset-centric Analysis}, booktitle={Proceedings of the European Chapter of the ACL (EACL)}, }

@article{daniluk2017frustratingly, journal={Proceedings of International Conference on Learning Representations (ICLR)}, month={November}, year={2017}, author={Daniluk, Michal and Rocktaschel, Tim and Welbl, Johannes and Riedel, Sebastian}, title={Frustratingly Short Attention Spans in Neural Language Modeling}, }

@inproceedings{welbl2017wnut, location={Copenhagen, Denmark}, title={Crowdsourcing Multiple Choice Science Questions}, booktitle={Workshop on Noisy User-generated Text}, year={2017}, author={Johannes Welbl, Nelson F. Liu, and Matt Gardner}, }

@inproceedings{Saeidi2016, month={December}, year={2016}, author={Saeidi, Marzieh and Bouchard, Guillaume and Liakata, Maria and Riedel, Sebastian}, title={SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods}, booktitle={Proceedings of the 26th International Conference on Computational Linguistics (COLING)}, }

@inproceedings{augenstein2016stance, month={November}, year={2016}, author={Augenstein, Isabelle and Rocktaschel, Tim and Vlachos, Andreas and Bontcheva, Kalina}, title={Stance Detection with Bidirectional Conditional Encoding}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, }

@inproceedings{spithourakis2016numerically, month={November}, year={2016}, author={Spithourakis, Georgios and Augenstein, Isabelle and Riedel, Sebastian}, title={Numerically Grounded Language Models for Semantic Error Correction}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, }

@inproceedings{Demeester2016lifted, month={November}, year={2016}, author={Demeester, Thomas and Rocktaschel, Tim and Riedel, Sebastian}, title={Lifted Rule Injection for Relation Embeddings}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, }

@inproceedings{Bouchard2016learning, month={November}, year={2016}, author={Bouchard, Guillaume and Stenetorp, Pontus and Riedel, Sebastian}, title={Learning to Generate Textual Data}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, }

@inproceedings{spithourakis2016clinical, month={November}, year={2016}, author={Spithourakis, Georgios and Petersen, Steffen and Riedel, Sebastian}, title={Clinical Text Prediction with Numerically Grounded Conditional Language Models}, booktitle={Proceedings of 7th International Workshop on Health Text Mining and Information Analysis (LOUHI) EMNLP}, }

@inproceedings{Parasca2016words, webnote={Best proposal award at RepEval}, month={August}, year={2016}, author={Parasca, Iuliana-Elena and Rauter, Andreas Lukas and Roper, Jack and Rusinov, Aleksandar and Bouchard, Guillaume and Riedel, Sebastian and Stenetorp, Pontus}, title={Defining Words with Words: Beyond the Distributional Hypothesis}, booktitle={Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP (RepEval)}, }

@inproceedings{li2016persona, month={August}, year={2016}, author={Li, Jiwei and Galley, Michel and Brockett, Chris and Spithourakis, Georgios and Gao, Jianfeng and Dolan, Bill}, title={A Persona-Based Neural Conversation Model}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL)}, }

@article{Kolesnyk2016Generating, journal={arXiv preprint arXiv:1606.01404}, month={June}, year={2016}, author={Kolesnyk, Vladyslav and Rocktaschel, Tim and Riedel, Sebastian}, title={Generating Natural Language Inference Chains}, }

@inproceedings{trouillon2016complex, month={June}, year={2016}, author={Trouillon, Théo and Welbl, Johannes and Bouchard, Guillaume and Riedel, Sebastian and Gaussier, Eric}, title={Complex Embeddings for Simple Link Prediction}, booktitle={International Conference on Machine Learning}, }

@inproceedings{demeester2016regularizing, month={June}, year={2016}, author={Demeester, Thomas and Rocktaschel, Tim and Riedel, Sebastian}, booktitle={Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC)}, title={Regularizing Relation Representations by First-order Implications}, }

@inproceedings{rocktaschel2016reasoning, month={February}, year={2016}, author={Rocktaschel, Tim and Grefenstette, Edward and Hermann, Karl Moritz and Kocisky, Tomas and Blunsom, Phil}, booktitle={International Conference on Learning Representations (ICLR)}, title={Reasoning about Entailment with Neural Attention}, }

@inproceedings{rocktaschel2016learning, month={June}, year={2016}, author={Rocktaschel, Tim and Riedel, Sebastian}, booktitle={Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC)}, title={Learning Knowledge Base Inference with Neural Theorem Provers}, }

@inproceedings{shimaoka2016attentive, month={June}, year={2016}, author={Shimaoka, Sonse and Stenetorp, Pontus and Inui, Kentaro and Riedel, Sebastian}, booktitle={Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC)}, title={An Attentive Neural Architecture for Fine-grained Entity Type Classification}, }

@inproceedings{welbl2016factorization, month={June}, year={2016}, author={Welbl, Johannes and Bouchard, Guillaume and Riedel, Sebastian}, booktitle={Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC)}, title={A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion}, }

@article{bosnjak2016programming, journal={arXiv preprint arXiv:1605.06640}, month={May}, year={2016}, author={Bosnjak, Matko and Rocktaschel, Tim and Naradowsky, Jason and Riedel, Sebastian}, title={Programming with a Differentiable Forth Interpreter}, }

@inproceedings{neumann2016, month={December}, year={2016}, author={Neumann, Mark and Stenetorp, Pontus and Riedel, Sebastian}, title={Learning to Reason With Adaptive Computation}, booktitle={Proceedings of Interpretable ML for Complex Systems NIPS Workshop}, }

@article{bhoopchand2016learning, journal={arXiv preprint arXiv:1605.06640}, month={May}, year={2016}, author={Bhoopchand, Avishkar and Rocktaschel, Tim and Barr, Earl and Riedel, Sebastian}, title={Learning Python Code Suggestion with a Sparse Pointer Network}, }

@inproceedings{Eisner2016, webnote={Best Paper at SocialNLP}, month={November}, year={2016}, author={Eisner, Ben and Rocktaschel, Tim and Augenstein, Isabelle and Bosnjak, Matko and Riedel, Sebastian}, title={emoji2vec: Learning Emoji Representations from their Description}, booktitle={Proceedings of the International Workshop on Natural Language Processing for Social Media at EMNLP (SocialNLP)}, }

@inproceedings{vlachos2015fact, month={September}, year={2015}, author={Vlachos, Andreas and Riedel, Sebastian}, title={Identification and Verification of Simple Claims about Statistical Properties}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing}, }

@inproceedings{augenstein2015ie, month={September}, year={2015}, author={Augenstein, Isabelle and Vlachos, Andreas and Maynard, Diana}, title={Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing}, }

@inproceedings{smith2015mctest, month={September}, year={2015}, author={Smith, Ellery and Greco, Nicola and Bosnjak, Matko and Vlachos, Andreas}, title={A Strong Lexical Matching Method for the Machine Comprehension Test}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing}, }

@inproceedings{mirowski2015deprnn, month={July}, year={2015}, author={Mirowski, Piotr and Vlachos, Andreas}, title={Dependency Recurrent Neural Language Models for Sentence Completion}, booktitle={Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference of the Asian Federation of Natural Language Processing}, }

@inproceedings{sanchez2015towards, month={March}, year={2015}, author={Sanchez, Ivan and Rocktaschel, Tim and Riedel, Sebastian and Singh, Sameer}, title={Towards Extracting Faithful and Descriptive Representations of Latent Variable Models}, booktitle={AAAI Spring Symposium on Knowledge Representation and Reasoning}, }

@inproceedings{singh2015towards, month={June}, title={Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction}, booktitle={NAACL Workshop on Vector Space Modeling for NLP (VSM)}, year={2015}, author={Singh, Sameer and Rocktaschel, Tim and Riedel, Sebastian}, }

@inproceedings{rocktaschel2015injecting, month={June}, year={2015}, author={Rocktaschel, Tim and Singh, Sameer and Riedel, Sebastian}, title={Injecting Logical Background Knowledge into Embeddings for Relation Extraction}, booktitle={Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, }

@inproceedings{Sanchez2015Workshop, month={March}, year={2015}, author={Sanchez, Ivan and Riedel, Sebastian}, title={Extracting interpretable models from matrix factorization models}, booktitle={Proceedings of the International Conference on Cognitive Computation}, }

@inproceedings{vlachos2014application, month={August}, year={2014}, author={Vlachos, Andreas and Clark, Stephen}, title={Application-Driven Relation Extraction with Limited Distant Supervision}, booktitle={Proceedings of the 25th international conference on Computational Linguistics (COLING)}, }

@inproceedings{riedel14wolfe, month={July}, year={2014}, author={Sebastian Riedel and Sameer Singh and Vivek Srikumar and Tim Rocktaschel and Larysa Visengeriyeva and Jan Noessner}, booktitle={International Workshop on Statistical Relational AI (StarAI)}, title={WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming}, }

@inproceedings{belanger14message, month={July}, year={2014}, author={David Belanger and Alexandre Passos and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={/publications/papers/boxDD_uai.pdf}, booktitle={Proceedings of the 30th Annual Conference on Uncertainty in AI (UAI)}, title={Message Passing for Soft Constraint Dual Decomposition}, }

@inproceedings{vlachos2014fact, month={July}, year={2014}, author={Vlachos, Andreas and Riedel, Sebastian}, title={Fact Checking: Task definition and dataset construction}, booktitle={Proceedings of the ACL Workshop on Language Technology and Computational Social Science}, }

@inproceedings{rocktaschel14low, webnote={Google Exeptional Submission Award}, month={June}, year={2014}, author={Rocktaschel, Tim and Bosnjak, Matko and Singh, Sameer and Riedel, Sebastian}, bdsk-url-1={http://sp14.ws/pub/rbsr-sp14-2014.pdf}, booktitle={ACL Workshop on Semantic Parsing (SP)}, title={Low-Dimensional Embeddings of Logic}, }

@inproceedings{singh2014designing, month={December}, title={Designing an IDE for Probabilistic Programming: Challenges and a Prototype}, booktitle={NIPS Workshop on Probabilistic Programming}, year={2014}, author={Singh, Sameer and Riedel, Sebastian and Hewitt, Luke and Rocktaschel, Tim}, }

@article{vlachos2014new, volume={2}, journal={Transactions of the Association for Computational Linguistics}, pages={547--559}, month={December}, year={2014}, author={Vlachos, Andreas and Clark, Stephen}, title={A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing}, }

@inproceedings{singh2013anytime, month={December}, year={2013}, author={Sameer Singh and Sebastian Riedel and Andrew McCallum}, booktitle={Neural Information Processing Systems (NIPS) Workshop on Resource Efficient Machine Learning}, title={Anytime Belief Propagation Using Sparse Domains}, }

@inproceedings{singh2013joint, month={October}, year={2013}, author={Sameer Singh and Sebastian Riedel and Brian Martin and Jiaping Zheng and Andrew McCallum}, booktitle={CIKM Workshop on Automated Knowledge Base Construction (AKBC)}, title={Joint Inference of Entities, Relations, and Coreference}, }

@inproceedings{riedel2013latent, month={May}, year={2013}, author={Riedel, Sebastian and Yao, Limin and McCallum, Andrew}, booktitle={International Conference on Learning Representations}, title={Latent Relation Representations for Universal Schemas}, }

@inproceedings{bui13automorphism, month={July}, year={2013}, author={Hung Bui and Tuyen N. Huynh and Sebastian Riedel}, booktitle={Proceedings of the 29th Annual Conference on Uncertainty in AI (UAI)}, title={Automorphism Groups of Graphical Models and Lifted Variational Inference}, }

@inproceedings{suchanek13advances, month={March}, year={2013}, author={Fabian Suchanek and James Fan and Raphael Hoffmann and Sebastian Riedel and Partha Pratim Talukdar}, bdsk-url-1={http://suchanek.name/work/publications/sigmodrec2013akbc}, booktitle={SIGMOD Records journal}, title={Advances in Automated Knowledge Base Construction}, }

@inproceedings{riedel13relation, month={June}, year={2013}, author={Sebastian Riedel and Limin Yao and Benjamin M. Marlin and Andrew McCallum}, bdsk-url-1={/publications/papers/riedel13relation.pdf}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, title={Relation Extraction with Matrix Factorization and Universal Schemas}, }

@inproceedings{belanger12map, webnote={Winner of the 2nd best spotlight presentation award at the NYC Machine Learning Symposium}, month={November}, year={2012}, author={David Belanger and Alexandre Passos and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://www.ic.unicamp.br/~tachard/docs/nips12.pdf}, booktitle={Advances in Neural Information Processing Systems (NIPS)}, title={MAP Inference in Chains using Column Generation}, }

@inproceedings{bui12automorphismextended, note={Extended version of the paper to appear in Statistical Relational AI (StaRAI-12) workshop at UAI '12}, webnote={Extended version of the paper to appear in Statistical Relational AI (StaRAI-12) workshop at UAI '12}, month={July}, year={2012}, author={Hung Bui and Tuyen N. Huynh and Sebastian Riedel}, bdsk-url-1={http://arxiv.org/pdf/1207.4814v1.pdf}, booktitle={arXiv.org}, title={Automorphism Groups of Graphical Models and Lifted Variational Inference}, }

@inproceedings{bui12automorphism, month={August}, year={2012}, author={Hung Bui and Tuyen N. Huynh and Sebastian Riedel}, bdsk-url-1={http://tsi.wfubmc.edu/labs/strait/StaRAI/accepted/bui.pdf}, booktitle={Second Statistical Relational AI (StaRAI-12) workshop at UAI}, title={Automorphism Groups of Graphical Models and Lifted Variational Inference}, }

@inproceedings{yao12unsupervised, month={July}, year={2012}, author={Limin Yao and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://ciir-publications.cs.umass.edu/pub/web/getpdf.php?id=1032}, booktitle={Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL)}, title={Unsupervised Relation Discovery with Sense Disambiguation}, }

@inproceedings{yao12probabilistic, month={June}, year={2012}, author={Limin Yao and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://ciir-publications.cs.umass.edu/getpdf.php?id=1061}, booktitle={Proceedings of the AKBC-WEKEX Workshop at NAACL}, title={Probabilistic Databases of Universal Schema}, }

@inproceedings{riedel12parse, month={July}, year={2012}, author={Sebastian Riedel and David A. Smith and Andrew McCallum}, bdsk-url-1={/publications/papers/riedel12parse.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Parse, Price and Cut - Delayed Column and Row Generation for Graph Based Parsers}, }

@inproceedings{narad12marg, address={Jeju, Korea}, month={July}, year={2012}, author={Jason Naradowsky and Sebastian Riedel and David A. Smith}, publisher={Association for Computational Linguistics}, bdsk-url-1={http://www.cs.umass.edu/~narad/_papers/relmarg_emnlp2012.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Improving NLP through Marginalization of Hidden Syntactic Structure}, }

@article{mclosky12combining, year={2012}, author={David McClosky and Sebastian Riedel and Mihai Surdeanu and Andrew McCallum and Christopher D. Manning}, bdsk-url-1={http://www.biomedcentral.com/1471-2105/13/S11/S9}, abstract={BACKGROUND:We explore techniques for performing model combination between the UMass and Stanford biomedical event extraction systems. Both sub-components address event extraction as a structured prediction problem, and use dual decomposition (UMass) and parsing algorithms (Stanford) to find the best scoring event structure. Our primary focus is on stacking where the predictions from the Stanford system are used as features in the UMass system. For comparison, we look at simpler model combination techniques such as intersection and union which require only the outputs from each system and combine them directly.RESULTS:First, we find that stacking substantially improves performance while intersection and union provide no significant benefits. Second, we investigate the graph properties of event structures, and their impact on the combination of our systems. Finally, we trace the origins of events proposed by the stacked model to determine the role each system plays in different components of the output. We learn that, while stacking can propose novel event structures not seen in either base model, these events have extremely low precision. Removing these novel events improves our already state-of-the-art F1 to 56.6% on the test set of Genia (Task 1). Overall, the combined system formed via stacking ("FAUST") performed well in the BioNLP 2011 shared task. The FAUST system obtained 1st place in three out of four tasks: 1st place in Genia Task 1 (56.0% F1) and Task 2 (53.9%), 2nd place in the Epigenetics and Post-translational Modifications track (35.0%), and 1st place in the Infectious Diseases track (55.6%).CONCLUSION:We present a state-of-the-art event extraction system that relies on the strengths of structured prediction and model combination through stacking. Akin to results on other tasks, stacking outperforms intersection and union and leads to very strong results. The utility of model combination hinges on complementary views of the data, and we show that our sub-systems capture different graph properties of event structures. Finally, by removing low precision novel events, we show that performance from stacking can be further improved.}, bdsk-url-2={http://dx.doi.org/10.1186/1471-2105-13-S11-S9}, title={Combining joint models for biomedical event extraction}, volume={13}, number={Suppl 11}, journal={BMC Bioinformatics}, pages={S9}, issn={1471-2105}, month={June}, doi={10.1186/1471-2105-13-S11-S9}, }

@inproceedings{belanger12column, month={June}, year={2012}, author={David Belanger and Alexandre Passos and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={/publications/papers/belanger12speeding.pdf}, booktitle={Inferning: Interactions between Inference and Learning, ICML Workshop}, title={Speeding up MAP with Column Generation and Block Regularization}, }

@article{yoshinobu11ucompare, year={2011}, author={Kano, Yoshinobu and Bjorne, Jari and Ginter, Filip and Salakoski, Tapio and Buyko, Ekaterina and Hahn, Udo and Cohen, K Bretonnel and Verspoor, Karin and Roeder, Christophe and Hunter, Lawrence and Kilicoglu, Halil and Bergler, Sabine and Van Landeghem, Sofie and Van Parys, Thomas and Van de Peer, Yves and Miwa, Makoto and Ananiadou, Sophia and Neves, Mariana and Pascual-Montano, Alberto and Ozgur, Arzucan and Radev, Dragomir and Riedel, Sebastian and Saetre, Rune and Chun, Hong-Woo and Kim, Jin-Dong and Pyysalo, Sampo and Ohta, Tomoko and Tsujii, Jun'ichi}, bdsk-url-1={http://www.biomedcentral.com/1471-2105/12/481}, abstract={BACKGROUND:Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.RESULTS:We have integrated nine event extraction systems in the U-Compare framework, making them inter-compatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. CONCLUSIONS:While individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble. }, bdsk-url-2={http://dx.doi.org/10.1186/1471-2105-12-481}, title={U-Compare bio-event meta-service: compatible BioNLP event extraction services}, pubmedid={22177292}, volume={12}, number={1}, journal={BMC Bioinformatics}, pages={481}, issn={1471-2105}, month={December}, doi={10.1186/1471-2105-12-481}, }

@article{riedel10bio, volume={27}, number={4}, journal={Computational Intelligence}, month={November}, year={2011}, author={Sebastian Riedel and Rune Saetre and Hong-Woo Chun and Toshihisa Takagi and Jun'ichi Tsujii}, title={Bio-Molecular Event Extraction with Markov Logic}, }

@inproceedings{klinger11interevent, month={September}, year={2011}, author={Roman Klinger and Sebastian Riedel and Andrew McCallum}, booktitle={Mining Complex Entities from Network and Biomedical Data (MIND), Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)}, title={Inter-Event Dependencies support Event Extraction from Biomedical Literature}, }

@inproceedings{yao11structured, month={July}, year={2011}, author={Limin Yao and Aria Haghighi and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://www.aclweb.org/anthology/D/D11/D11-1135.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Structured Relation Discovery using Generative Models}, }

@inproceedings{riedel11robust, webnote={A longer more informative version of this paper appeared in EMNLP 2011}, month={June}, year={2011}, author={Sebastian Riedel and Andrew McCallum}, bdsk-url-1={/publications/papers/riedel11robust.pdf}, booktitle={Proceedings of the Natural Language Processing in Biomedicine NAACL 2011 Workshop (BioNLP)}, title={Robust Biomedical Event Extraction with Dual Decomposition and Minimal Domain Adaptation}, }

@inproceedings{riedel11model, month={June}, year={2011}, author={Sebastian Riedel and David McClosky and Mihai Surdeanu and Christopher D. Manning and Andrew McCallum}, bdsk-url-1={/publications/papers/riedel11model.pdf}, booktitle={Proceedings of the Natural Language Processing in Biomedicine NAACL 2011 Workshop (BioNLP)}, title={Model Combination for Event Extraction in BioNLP}, }

@inproceedings{riedel11fast, note={Plenary Session Paper (acceptance rate 1.6\%)}, webnote={Plenary Session Paper (acceptance rate 1.6%)}, month={July}, year={2011}, author={Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://aclweb.org/anthology/D/D11/D11-1001.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Fast and Robust Joint Models for Biomedical Event Extraction}, }

@inproceedings{yoshikawa10coreference, webnote={Best Paper Award}, month={October}, year={2010}, author={Katsumasa Yoshikawa and Sebastian Riedel and Tsutomu Hirao and Masayuki Asahara and Yuji Matsumoto}, booktitle={Proceedings of the Fourth International Symposium on Semantic Mining in Biomedicine (SMBM)}, title={Coreference Based Event-Argument Relation Extraction on Biomedical Text}, }

@inproceedings{yao10collective, month={October}, year={2010}, author={Limin Yao and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://www.cs.umass.edu/~lmyao/papers/relation-emnlp10.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Collective Cross-Document Relation Extraction Without Labelled Data}, }

@inproceedings{riedel10declarative, month={July}, year={2010}, author={Sebastian Riedel}, bdsk-url-1={http://maroo.cs.umass.edu/pub/web/getpdf.php?id=942}, booktitle={Statistical Relational AI workshop at AAAI (starAI)}, title={Declarative Probabilistic Programming for Undirected Models: Open Up to Scale Up}, }

@inproceedings{riedel10relaxed, address={Los Angeles, California}, year={2010}, author={Sebastian Riedel and David A. Smith}, bdsk-url-1={http://www.aclweb.org/anthology/N10-1117}, title={Relaxed Marginal Inference and its Application to Dependency Parsing}, pages={760--768}, month={June}, publisher={Association for Computational Linguistics}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, }

@inproceedings{riedel10modeling, month={September}, year={2010}, author={Sebastian Riedel and Limin Yao and Andrew McCallum}, bdsk-url-1={/publications/papers/riedel10modeling.pdf}, booktitle={Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)}, title={Modeling Relations and Their Mentions without Labeled Text}, }

@inproceedings{riedel10inference, month={July}, year={2010}, author={Sebastian Riedel and David Smith and Andrew McCallum}, booktitle={Proceedings of the 26th Annual Conference on Uncertainty in AI (UAI)}, title={Inference by Minimizing Size, Divergence, or their Sum}, }

@inproceedings{singh10constraint, address={Los Angeles, California}, year={2010}, author={Sameer Singh and Limin Yao and Sebastian Riedel and Andrew McCallum}, bdsk-url-1={http://www.cs.umass.edu/~lmyao/papers/semi-ie-naacl10.pdf}, title={Constraint-Driven Rank-Based Learning for Information Extraction}, pages={729--732}, month={June}, publisher={Association for Computational Linguistics}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, }

@inproceedings{riedel09markov, pages={41--49}, month={June}, year={2009}, author={Sebastian Riedel and Hong-Woo Chun and Toshihisa Takagi and Jun'ichi Tsujii}, bdsk-url-1={http://www.aclweb.org/anthology/W/W09/W09-1406.pdf}, booktitle={Proceedings of the Natural Language Processing in Biomedicine NAACL 2009 Workshop (BioNLP)}, title={A Markov Logic Approach to Bio-Molecular Event Extraction}, }

@inproceedings{yoshikawa09jointly, address={Suntec, Singapore}, year={2009}, author={Yoshikawa, Katsumasa and Riedel, Sebastian and Asahara, Masayuki and Matsumoto, Yuji}, bdsk-url-1={http://www.aclweb.org/anthology/P/P09/P09-1046}, title={Jointly Identifying Temporal Relations with Markov Logic}, pages={405--413}, month={August}, publisher={Association for Computational Linguistics}, booktitle={Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL '09)}, }

@inproceedings{riedel09cutting, keywords={Markov Logic}, month={July}, year={2009}, author={Sebastian Riedel}, bdsk-url-1={http://alchemy.cs.washington.edu/papers/pdfs/riedel09.pdf}, booktitle={SRL 2009}, title={Cutting Plane MAP Inference for Markov Logic}, }

@inproceedings{riedel09revisiting, address={Morristown, NJ, USA}, year={2009}, author={Sebastian Riedel and James Clarke}, bdsk-url-1={http://l2r.cs.uiuc.edu/~danr/Papers/RiedelCl09.pdf}, title={Revisiting optimal decoding for machine translation IBM model 4}, pages={5--8}, month={June}, publisher={Association for Computational Linguistics}, location={Boulder, Colorado}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, }

@phdthesis{riedel09efficient, month={May}, school={University of Edinburgh}, year={2009}, author={Sebastian Riedel}, title={Efficient Prediction of Relational Structure and its Application to Natural Language Processing}, }

@inproceedings{yoshikawa09joint, keywords={Markov Logic,Temporal}, month={May}, year={2009}, author={Katsumasa Yoshikawa and Sebastian Riedel and Masayuki Asahara and Yuji Matsumoto}, booktitle={Transactions of the Japanese Society for Artificial Intelligence}, title={Joint Inference of Temporal Relations with Markov Logic}, }

@inproceedings{meza09jointly, month={June}, year={2009}, author={Ivan Meza-Ruiz and Sebastian Riedel}, bdsk-url-1={http://www.aclweb.org/anthology/N/N09/N09-1018.pdf}, booktitle={Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)}, title={Jointly Identifying Predicates, Arguments and Senses using Markov Logic}, }

@inproceedings{riedel08collective, pages={193--197}, keywords={Markov Logic, Semantic Role Labelling, NLP}, month={August}, year={2008}, author={Sebastian Riedel and Ivan Meza-Ruiz}, bdsk-url-1={http://www.aclweb.org/anthology/W/W08/W08-2125.pdf}, booktitle={Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL)}, title={Collective Semantic Role Labelling with Markov Logic}, }

@inproceedings{mezaruiz08accurate, pages={5021--5024}, month={March}, year={2008}, author={Ivan Vladimir Meza-Ruiz and Sebastian Riedel and Oliver Lemon}, bdsk-url-1={http://alchemy.cs.washington.edu/papers/meza-ruiz08b/meza-ruiz08b.pdf}, booktitle={Proceedings of 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, title={Accurate Statistical Spoken Language Understanding From Limited Development Resources}, }

@misc{mezaruiz08spoken, howpublished={Late Breaking Abstracts of Londial: Workshop on the semantics and pragmatics of dialogue}, month={June}, year={2008}, author={Ivan Meza-Ruiz and Sebastian Riedel and Oliver Lemon}, bdsk-file-1={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}, title={Spoken Language Understanding in dialogue systems, using a 2-layer Markov Logic Network: improving semantic accuracy}, }

@inproceedings{riedel08improving, pages={468--475}, year={2008}, author={Sebastian Riedel}, bdsk-url-1={http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.143.7914&rep;=rep1&type;=pdf}, mont={July}, booktitle={Proceedings of the 24th Annual Conference on Uncertainty in AI (UAI)}, title={Improving the accuracy and Efficiency of MAP Inference for Markov Logic}, }

@inproceedings{nivre07shared, pages={915---932}, month={June}, year={2007}, author={J. Nivre and J. Hall and S. Kubler and R. McDonald and J. Nilsson and S. Riedel and D. Yuret}, bdsk-url-1={http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.4778&rep;=rep1&type;=pdf}, booktitle={Conference on Empirical Methods in Natural Language Processing and Natural Language Learning}, title={The CoNLL Shared Task on Dependency Parsing}, }

@inproceedings{riedel06incremental, pages={129--137}, month={July}, year={2006}, author={Sebastian Riedel and James Clarke}, bdsk-file-1={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}, bdsk-url-1={http://aclweb.org/anthology-new/W/W06/W06-1616.pdf}, booktitle={Proceedings of the Conference on Empirical methods in natural language processing (EMNLP)}, title={Incremental Integer Linear Programming for Non-projective Dependency Parsing}, }

@inproceedings{riedel06multi, pages={226--230}, month={June}, year={2006}, author={Sebastian Riedel and Ruken Cakeci and Ivan Meza-Ruiz}, bdsk-url-1={http://portal.acm.org/citation.cfm?id=1596276.1596319}, booktitle={Proceedings of CoNLL}, title={Multi-lingual Dependency Parsing with Incremental Integer Linear Programming}, }

@inproceedings{riedel05genic, pages={69--74}, month={August}, year={2005}, author={Sebastian Riedel and Ewan Klein}, bdsk-url-1={http://www.cs.york.ac.uk/aig/lll/lll05/lll05-riedel.pdf}, booktitle={Learning Language in Logic Workshop, ICML}, title={Genic Interaction Extraction with Semantic and Syntactic Chains}, }

@inproceedings{lehel04linkage, pages={346-350}, month={September}, year={2004}, author={Vanda Lehel and Florian Matthes and Sebastian Riedel}, bdsk-url-1={http://subs.emis.de/LNI/Proceedings/Proceedings50/GI-Proceedings.50-74.pdf}, booktitle={GI Jahrestagung (1)}, title={Linkage Flooding: Ein Algorithmus zur dateninhaltsorientierten Fusion in vernetzten Informationsbest�nden.}, }

@inproceedings{grover04framework, editor={Simon J. Cox}, address={Nottingham, UK}, year={2004}, author={Claire Grover and Harry Halpin and Ewan Klein and Jochen L. Leidner and Stephen Potter and Sebastian Riedel and Sally Scrutchin and Richard Tobin}, isbn={1-904425-21-6}, bdsk-url-1={http://www.iccs.informatics.ed.ac.uk/~ewan/Papers/Grover:2004:FTM.pdf}, title={A Framework for Text Mining Services}, pages={878--885}, month={August}, booktitle={Proceedings of the UK e-Science Programme All Hands Meeting 2004 (AHM)}, }

@mastersthesis{riedel04towards, school={University of Edinburgh}, year={2004}, author={Sebastian Riedel}, title={Towards Structural Evolution of Classification Workflows}, }

@mastersthesis{riedel03entwicklung, school={Technische Universitaet Hamburg-Harburg}, year={2003}, author={Sebastian Riedel}, bdsk-url-1={http://wwwmatthes.in.tum.de/file/Publications/2003/Ried03/Ried03.pdf}, title={Entwicklung eines Modells, Verfahrens und softwaretechnischen Rahmenwerks f�r Record Linkage in semantischen Netzen}, }

@inproceedings{genc02marker-less, address={Washington, DC, USA}, year={2002}, author={Y. Genc and S. Riedel and F. Souvannavong and C. Akinlar and N. Navab}, isbn={0-7695-1781-1}, bdsk-url-1={http://fabrice.souvannavong.free.fr/Prof/ismar02.pdf}, bdsk-url-2={/publications/papers/genc02marker-less.pdf}, title={Marker-less Tracking for AR: A Learning-Based Approach}, pages={295--304}, month={September}, publisher={IEEE Computer Society}, booktitle={ISMAR '02: Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR)}, }