Jonathan Bragg


2022

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GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Daniel Khashabi | Gabriel Stanovsky | Jonathan Bragg | Nicholas Lourie | Jungo Kasai | Yejin Choi | Noah A. Smith | Daniel Weld
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on producing consistent evaluations that are reproducible‚ÄĒover time and across different populations. We study this goal in different stages of the human evaluation pipeline. In particular, we consider design choices for the annotation interface used to elicit human judgments and their impact on reproducibility. Furthermore, we develop an automated mechanism for maintaining annotator quality via a probabilistic model that detects and excludes noisy annotators. Putting these lessons together, we introduce GENIE: a system for running standardized human evaluations across different generation tasks. We instantiate GENIE with datasets representing four core challenges in text generation: machine translation, summarization, commonsense reasoning, and machine comprehension. For each task, GENIE offers a leaderboard that automatically crowdsources annotations for submissions, evaluating them along axes such as correctness, conciseness, and fluency. We have made the GENIE leaderboards publicly available, and have already ranked 50 submissions from 10 different research groups. We hope GENIE encourages further progress toward effective, standardized evaluations for text generation.

2016

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Effective Crowd Annotation for Relation Extraction
Angli Liu | Stephen Soderland | Jonathan Bragg | Christopher H. Lin | Xiao Ling | Daniel S. Weld
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies