Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and…
SemEval-2019 Task 10: Math Question Answering
We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a…
Variational Pretraining for Semi-supervised Text Classification
We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational…
Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a…
A General Framework for Information Extraction Using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by…
Aligning Vector-spaces with Noisy Supervised Lexicons
The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the…
Benchmarking Hierarchical Script Knowledge
Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, “boiling pasta” is a sub-event of “making a pasta dish”, typically…
Combining Distant and Direct Supervision for Neural Relation Extraction
In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to…
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant…
DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion
Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models.…