Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Evaluating Question Answering Evaluation
As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to…
On Making Reading Comprehension More Comprehensive
Machine reading comprehension, the task of evaluating a machine’s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted…
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study…
Reasoning Over Paragraph Effects in Situations
A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we…
A Discrete Hard EM Approach for Weakly Supervised Question Answering
Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting…
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate…
BERT for Coreference Resolution: Baselines and Analysis
We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared…
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel…
COSMOS QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper,…
Counterfactual Story Reasoning and Generation
Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a necessary component of…