44 citations · 68 across the 4 of their papers we have counts for
7 papers
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William W. Cohen +1
Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generat…
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth +2
To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential. While modern dialogue agents excel…
Simple and Efficient ways to Improve REALM
Vidhisha Balachandran, Ashish Vaswani, Yulia Tsvetkov +1
Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25. REALM (Guu et al., 2020)…
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov
Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically g…
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers
Dheeraj Rajagopal, Vidhisha Balachandran, Eduard Hovy +1
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts. SelfExplain augments existing neural classifiers…
StructSum: Summarization via Structured Representations
Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee +3
Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstracti…