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Register a new userFlowQA: Grasping Flow in History for Conversational Machine Comprehension
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Hsin-Yuan Huang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
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Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). However, the internal mechanism of these artifacts still remains unclear, placing an obstacle for further understanding these models. This paper focuses on conducting a series of analytical experiments to examine the relations between the multi-head self-attention and the final performance, trying to analyze the potential explainability in PLM-based MRC models. We perform quantitative analyses on SQuAD (English) and CMRC 2018 (Chinese), two span-extraction MRC datasets, on top of BERT, ALBERT, and ELECTRA in various aspects. We discover that {em passage-to-question} and {em passage understanding} attentions are the most important ones, showing strong correlations to the final performance than other parts. Through visualizations and case studies, we also observe several general findings on the attention maps, which could be helpful to understand how these models solve the questions.
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