67 citations · 217 across the 55 of their papers we have counts for
5 papers · 1 filter
Abstractive Summarization Guided by Latent Hierarchical Document Structure
Yifu Qiu, Shay B. Cohen
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to…
Understanding Domain Learning in Language Models Through Subpopulation Analysis
Zheng Zhao, Yftah Ziser, Shay B. Cohen
We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of…
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
Balint Gyevnar, Massimiliano Tamborski, Cheng Wang +3
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and q…
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
Marcio Fonseca, Yftah Ziser, Shay B. Cohen
We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers. Our method, Facto…
Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information
Shun Shao, Yftah Ziser, Shay B. Cohen
We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove private or guarded information from neural representations. Our method uses matrix decompositi…