Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Interactive Extractive Search over Biomedical Corpora
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token…
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this…
A Two-Stage Masked LM Method for Term Set Expansion
We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical utility, and also of…
Unsupervised Domain Clusters in Pretrained Language Models
The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In addition, domain…
Nakdan: Professional Hebrew Diacritizer
We present a system for automatic diacritization of Hebrew text. The system combines modern neural models with carefully curated declarative linguistic knowledge and comprehensive manually…
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-ofthe-art models in grounded…
Procedural Reading Comprehension with Attribute-Aware Context Flow
Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading…
What's Hidden in a Randomly Weighted Neural Network?
Training a neural network is synonymous with learning the values of the weights. In contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive…
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
In this paper, we introduce the Butterfly Transform (BFT), a light weight channel fusion method that reduces the computational complexity of point-wise convolutions from O(n^2) of conventional…
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions…