Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various…
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those…
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training
We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at…
Mixture Content Selection for Diverse Sequence Generation
Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target…
PaLM: A Hybrid Parser and Language Model
We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised constituency parser…
Pretrained Language Models for Sequential Sentence Classification
As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in…
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions
We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen with a higher SPF…
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential…
RNN Architecture Learning with Sparse Regularization
Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning…
SciBERT: A Pretrained Language Model for Scientific Text
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to…