Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated…
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…
Iterative Search for Weakly Supervised Semantic Parsing
Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical…
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets
Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared…
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…
Text Generation from Knowledge Graphs with Graph Transformers
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive…
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs. Due to annotation challenges,…
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…
Neural network gradient-based learning of black-box function interfaces
Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions…
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a…