News
- 25/02/2020:
Our new work in collaboration with NYU and FAIR on Unsupervised Question Decomposition for multi-hop QA is now up on ArXiv. Break down complex questions into a list of simple questions without any labelled data. Strong, robust results on HotPotQA, and don’t need any supporting fact annotation.
- 16/10/2019:
We’re releasing a new cross-lingual Question Answering dataset, MLQA: Evaluating Cross-lingual Extractive Question Answering Check out the dataset here
- 03/09/2019:
Language Models as Knowledgebases? accepted at EMNLP 2019! Code for the paper’s experiments, and the LAMA probe are available here
- 13/06/2019:
Unsupervised Question Answering by Cloze Translation accepted at ACL 2019! Code and synthetic training data will be available here
- 13/06/2019:
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language accepted at ACL 2019! Code and synthetic training data is available here
- 28/09/2018:
Interpretation of Natural Language Rules in Conversational Machine Reading accepted at EMNLP 2018 - CodaLab challenge are now LIVE at sharc-data.github.io!
- 26/09/2018:
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection accepted at EMNLP 2018 - code available at this link!
People
Sebastian Riedel
Professor
Pontus Stenetorp
Lecturer
Tim Rocktäschel
Lecturer
Ed Grefenstette
Honorary Reader
Pasquale Minervini
Senior Research Associate
Matko Bošnjak
Final Year PhD Student
Johannes Welbl
Final Year PhD Student
Tom Crossland
PhD Student
Patrick Lewis
1st Year PhD Student
Yuxiang Wu
1st Year PhD Student
Max Bartolo
PhD Student
Minqi Jiang
PhD student
Yihong Chen
1st Year PhD Student
Alastair Roberts
Visiting Researcher
Zhao Zhang
Visiting PhD Student
MSc Students
Zhengyao Jiang
Mres Computational Statistics and Machine Learning
Alumni
Andreas Vlachos
Now a senior lecturer at University of Cambridge
Luke Hewitt
Now a PhD student at MIT
Gerasimos Lampouras
Now a research associate at University of Sheffield
Saku Sugawara
Saku is a Ph.D. student at the University of Tokyo, interested in natural language understanding by machines.
Sonse Shimaoka
Now a master student at Tohoku University
Guillaume Bouchard
Now a Research Manager at Facebook
Thomas Demeester
Now a post-doc at University of Ghent
Jason Naradowsky
Now a research scientist at Preferred Networks (PFN)
Théo Trouillon
Now back to being a PhD student at Xerox Research Centre Europe
Marzieh Saeidi
Now a Research Scientist at Facebook
Isabelle Augenstein
Now an assistant professor at University of Copenhagen
Naoya Inoue
Now an assistant professor at Tohoku University
Tim Dettmers
Now a PhD student at University of Washington
V. Ivan Sanchez
Now an NLP researcher at Lenovo
Andres Campero
Now back to being a PhD student at MIT
Takuma Yoneda
Now a student at Toyota Technological Institute at Chicago
Georgios Spithourakis
Now a ML engineer at PolyAI
Publications
Software
Cape is an open source large-scale open-domain Question Answering system.
stat-nlp-book is an interactive Statistical NLP book in Python, used for our StatNLP from 2016 onwards.
stat-nlp-book-scala is an interactive Statistical NLP book in Scala, used for our StatNLP course in 2015 / 16.
Jack the Reader is a Machine Reading framework for Question Answering, Natural Language Inference, and Link Prediction - see the paper here.
Neural Theorem Prover is an end-to-end differentiable logic reasoner, implementing the model described in End-to-end Differentiable Proving.
Inferbeddings is a link prediction framework that allows including First-Order background knowledge via adversarial training - the model is described in Adversarial Sets for Regularising Neural Link Predictors.
wolfe is a framework for building rich machine learning models, based on functional programming, factor graphs, optimization and composition.
ucleed is a biomedical event extractor that ranked first in several tracks of the BioNLP 2011 shared task.
thebeast is a Markov Logic inference and learning engine.
What’s Wrong With My NLP? is a visualizer for NLP problems.
Datasets
MLQA
A multi-way aligned extractive QA evaluation benchmark MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.
ShARC: Shaping Answers with Rules through Conversation
A collection of 32k task instances based on real-world rules and crowd-generated questions and scenarios requiring both the interpretation of rules and the application of background knowledge.
WikiHop & MedHop (QAngaroo)
Multi-hop question answering datasets from two different domains, designed to enabe models to combine disjoint pieces of textual evidence.