Neha Nayak Kennard — accepted AI-conference papers

2023

Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies

Raymond Zhang, Neha Nayak Kennard, Daniel Smith, Daniel McFarland, Andrew McCallum, Katherine Keith

ACL 2023findings

A promising approach to estimate the causal effects of peer review policies is to analyze data from publication venues that shift policies from single-blind to double-blind from one year to the next. However, in these settings the content of the manuscript is a confounding variable—each year has a d…

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2022

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

Neha Nayak Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton +3

NAACL 2022long

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 rebutta…

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