Search Results for author: Vidhisha Balachandran

Found 9 papers, 5 papers with code

Investigating the Effect of Background Knowledge on Natural Questions

no code implementations NAACL (DeeLIO) 2021 Vidhisha Balachandran, Bhuwan Dhingra, Haitian Sun, Michael Collins, William Cohen

We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.

Unsupervised Keyphrase Extraction via Interpretable Neural Networks

no code implementations15 Mar 2022 Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov

Keyphrase extraction aims at automatically extracting a list of "important" phrases which represent the key concepts in a document.

Keyphrase Extraction Topic Classification

DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues

1 code implementation ICLR 2021 Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth, Alan Black, Yulia Tsvetkov

To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential.

Response Generation

Simple and Efficient ways to Improve REALM

no code implementations EMNLP (MRQA) 2021 Vidhisha Balachandran, Ashish Vaswani, Yulia Tsvetkov, Niki Parmar

Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25.

SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers

2 code implementations EMNLP 2021 Dheeraj Rajagopal, Vidhisha Balachandran, Eduard Hovy, Yulia Tsvetkov

We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.

Text Classification

StructSum: Summarization via Structured Representations

1 code implementation EACL 2021 Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov

To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.

Abstractive Text Summarization Document Summarization

Differentiable Reasoning over a Virtual Knowledge Base

1 code implementation ICLR 2020 Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen

In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.

Re-Ranking

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