Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Probing Across Time: What Does RoBERTa Know and When?
Models of language trained on very large corpora have been demonstrated useful for NLP. As fixed artifacts, they have become the object of intense study, with many researchers “probing” the extent…
CDLM: Cross-Document Language Modeling
We introduce a new pretraining approach for language models that are geared to support multi-document NLP tasks. Our crossdocument language model (CD-LM) improves masked language modeling for these…
Explaining Answers with Entailment Trees
Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by not just listing supporting textual evidence (“rationales”), but also showing how such evidence…
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution
Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and…
How Much Coffee Was Consumed During EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI
Many real-world problems require the combined application of multiple reasoning abilities employing suitable abstractions, commonsense knowledge, and creative synthesis of problem-solving…
Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
Large language models have shown promising results in zero-shot settings (Brown et al., 2020; Radford et al., 2019). For example, they can perform multiple choice tasks simply by conditioning on a…
Sister Help: Data Augmentation for Frame-Semantic Role Labeling
While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data…
Finetuning Pretrained Transformers into RNNs
Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a significant computational cost, as the attention mechanism’s complexity scales…
Sentence Bottleneck Autoencoders from Transformer Language Models
Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders,…
Think about it! Improving defeasible reasoning by first modeling the question scenario
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a…