Hamed Zamani


2022

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DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard | Tim O’Gorman | Rajarshi Das | Akshay Sharma | Chhandak Bagchi | Matthew Clinton | Pranay Kumar Yelugam | Hamed Zamani | Andrew McCallum
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.

2019

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Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Rajarshi Das | Ameya Godbole | Dilip Kavarthapu | Zhiyu Gong | Abhishek Singhal | Mo Yu | Xiaoxiao Guo | Tian Gao | Hamed Zamani | Manzil Zaheer | Andrew McCallum
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to ‘hop’ to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the benchmark by 10.59 F1.

2015

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Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances
José G. C. de Souza | Hamed Zamani | Matteo Negri | Marco Turchi | Daniele Falavigna
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies