Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering

• Published in: Transactions of the Association for Computational Linguistics Vol. 7; pp. 387 - 401
• Main Authors: Wallace, Eric, Rodriguez, Pedro, Feng, Shi, Yamada, Ikuya, Boyd-Graber, Jordan
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.11.2019; MIT Press Journals, The; The MIT Press
• Subjects: Computers; Datasets; Human-computer interaction; Information retrieval; Language; Linguistics; Natural language; Natural language (computers); Natural language generation; Question answer sequences; Questions; User interface; Writing
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00279

Adversarial evaluation stress-tests a model’s understanding of natural language. Because past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human–computer matches: Although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.