Why Machine Reading Comprehension Models Learn Shortcuts?

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 989–1002 August 1–6, 2021. ©2021 Association for Computational Linguistics 989 Why Machine Reading Comprehension Models Learn Shortcuts? Yuxuan Lai, Chen Zhang, Yansong Feng∗, Quzhe Huang, and Dongyan Zhao Wangxuan Institute of Computer Technology, Peking University, China The MOE Key Laboratory of Computational Linguistics, Peking University, China {erutan, zhangch, fengyansong, huangquzhe, zhaody} @pku.edu.cn Abstract Recent studies report that many machine read- ing comprehension (MRC) models can per- form closely to or even better than humans on benchmark datasets. However, existing works indicate that many MRC models may learn shortcuts to outwit these benchmarks, but the performance is unsatisfactory in real-world ap- plications. In this work, we attempt to ex- plore, instead of the expected comprehension skills, why these models learn the shortcuts. Based on the observation that a large portion of questions in current datasets have shortcut solutions, we argue that larger proportion of shortcut questions in training data make mod- els rely on shortcut tricks excessively. To in- vestigate this hypothesis, we carefully design two synthetic datasets with annotations that indicate whether a question can be answered using shortcut solutions. We further propose two new methods to quantitatively analyze the learning difficulty regarding shortcut and challenging questions, and revealing the inher- ent learning mechanism behind the different performance between the two kinds of ques- tions. A thorough empirical analysis shows that MRC models tend to learn shortcut ques- tions earlier than challenging questions, and the high proportions of shortcut questions in training sets hinder models from exploring the sophisticated reasoning skills in the later stage of training. 1 Introduction The task of machine reading comprehension (MRC) aims at evaluating whether a model can un- derstand natural language texts by answering a se- ries of questions. Recently, MRC research has seen considerable progress in terms of model perfor- mance, and many models are reported to approach or even outperform human-level performance on ∗Corresponding author. P: ... Begun as a one-page journal in September 1876 , the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States� … Q.1: When did the Scholastic journal come out ? Coreference Paraphrasing Answer Question Word Matching Comprehension Challenge Shortcut Figure 1: An illustration of shortcuts in Machine Read- ing Comprehension. P is an excerpt of the original pas- sage. different benchmarks. These benchmarks are de- signed to address challenging features, such as evi- dence checking in multi-document inference (Yang et al., 2018), co-reference resolution (Dasigi et al., 2019), dialog understanding (Reddy et al., 2019), symbolic reasoning (Dua et al., 2019), and so on. However, recent analysis indicates that many MRC models unintentionally learn shortcuts to trick on specific benchmarks, while having infe- rior performance in real comprehension challenges (Sugawara et al., 2018). For example, when answer- ing Q.1 in Figure 1, we expect an MRC model to understand the semantic relation between come out and begun, and output the answer, September 1876, by bridging the co-reference …

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