ACL 2020 Announces Best Paper & Test-Of-Time Awards

Organizers of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) today announced their Best Paper Awards, with the Best Overall Paper going to Beyond Accuracy: Behavioral Testing of NLP Models with CheckList by researchers from Microsoft, University of Washington, and University of California-Irvine. The winning paper introduces CheckList, a task-agnostic methodology for testing NLP models that includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, and a software tool that can quickly generate a large number of diverse test cases. ACL announced two Honourable Mentions in the Overall Best Paper category: Don't Stop Pretraining: Adapt Language Models to Domains and Tasks by researchers from the Allen Institute for Artificial Intelligence and University of Washington. The honourable mentions are Don't Stop Pretraining: Adapt Language Models to Domains and Tasks by researchers from the Allen Institute for Artificial Intelligence and University of Washington; and Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics by researchers from the University of Melbourne. The ACL 2020 Test-of-Time Awards meanwhile went to the 1995 papers Centering: A Framework for Modeling the Local Coherence of Discourse by Barbara J. Grosz, Aravind K. Joshi, and Scott Weinstein; and Unsupervised Word Sense Disambiguation Rivaling Supervised Methodsby David Yarowsky; and the 2010 papers Distributional Memory: A General Framework for Corpus-based Semantics by Marco Baroni and Alessandro Lenci; and Word Representations: A Simple and General Method for Semi-supervised Learning by Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio.