UNIFIEDQA: Crossing Format Boundaries with a Single QA System

Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1896–1907 November 16 - 20, 2020. c⃝2020 Association for Computational Linguistics 1896 UNIFIEDQA: Crossing Format Boundaries with a Single QA System Daniel Khashabi1 Sewon Min2 Tushar Khot1 Ashish Sabharwal1 Oyvind Tafjord1 Peter Clark1 Hannaneh Hajishirzi1,2 1Allen Institute for AI, Seattle, U.S.A. 2University of Washington, Seattle, U.S.A. Abstract Question answering (QA) tasks have been posed using a variety of formats, such as ex- tractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA commu- nity. We argue that such boundaries are artifi- cial and perhaps unnecessary, given the reason- ing abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in language modeling to build a sin- gle pre-trained QA model, UNIFIEDQA, that performs well across 20 QA datasets spanning 4 diverse formats. UNIFIEDQA performs on par with 8 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed for- mats, UNIFIEDQA performs surprisingly well, showing strong generalization from its out-of- format training data. Finally, fine-tuning this pre-trained QA model into specialized mod- els results in a new state of the art on 10 fac- toid and commonsense QA datasets, establish- ing UNIFIEDQA as a strong starting point for building QA systems.1 1 Introduction Question answering is a common tool for assessing how well can computers understand language and reason with it. To this end, the NLP community has introduced several distinct datasets, with four popular QA formats illustrated in Fig. 1. For in- stance, some datasets expect the answer to be “yes” or “no”, or a unique answer span in the associated paragraph (as opposed to multiple or no spans). These differences have motivated their study in silos, often encoding QA format into the model ar- chitecture itself. Efforts to exploit multiple datasets remain largely restricted to a single format. For example, Clark et al. (2019c) limit consideration to 1 https://github.com/allenai/unifiedqa Extractive [SQuAD] Question: At what speed did the turbine operate? Context: (Nikola_Tesla) On his 50th birthday in 1906, Tesla demonstrated his 200 horsepower (150 kilowatts) 16,000 rpm bladeless turbine. ... Gold answer: 16,000 rpm Multiple-Choice [ARC-challenge] Question: What does photosynthesis produce that helps plants grow? Candidate Answers: (A) water (B) oxygen (C) protein (D) sugar Gold answer: sugar Yes/No [BoolQ] Question: Was America the first country to have a president? Context: (President) The first usage of the word president to denote the highest official in a government was during the Commonwealth of England ... Gold answer: no Abstractive [NarrativeQA] Question: What does a drink from narcissus's spring cause the drinker to do? Context: Mercury has awakened Echo, who weeps for Narcissus, and states that a drink from Narcissus's spring causes the drinkers to "Grow dotingly enamored of themselves." ... Gold answer: fall in love with themselves Figure 1: Four formats (color-coded throughout the paper) commonly used for posing questions and an- swering …

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