291 citations · 3k across the 73 of their papers we have counts for
7 papers · 1 filter
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Siddhant Arora, Danish Pruthi, Norman Sadeh +3
In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important.…
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call…
MATE: Multi-view Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller +1
This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. Howe…
Time-Aware Language Models as Temporal Knowledge Bases
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos +3
Many facts come with an expiration date, from the name of the President to the basketball team Lebron James plays for. But language models (LMs) are trained on snapshots of data co…
Iterative Hierarchical Attention for Answering Complex Questions over Long Documents
Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop questi…
What's the best place for an AI conference, Vancouver or ______: Why completing comparative questions is difficult
Avishai Zagoury, Einat Minkov, Idan Szpektor +1
Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. He…