128 citations · 290 across the 17 of their papers we have counts for
6 papers · 1 filter
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal +3
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tas…
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan +3
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve…
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
Srinivasan Iyer, Sewon Min, Yashar Mehdad +1
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive exa…
Efficient One-Pass End-to-End Entity Linking for Questions
Belinda Z. Li, Sewon Min, Srinivasan Iyer +2
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and G…
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer +8
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datas…
DeLighT: Deep and Light-weight Transformer
Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer +2
We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. D…