Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
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DOI：
10.18653/v1/2022.findings-acl.28
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Lingbo Mo;A. Lewis;Huan Sun;Michael White
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文献类型：
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作者：
Lingbo Mo;A. Lewis;Huan Sun;Michael White
现有的语义句法分析研究主要集中在将自然语言话语一次性映射到逻辑形式上。然而，由于自然语言可能包含歧义和可变性，这是一个困难的挑战。在这项工作中，我们研究了一个交互式语义分析框架，该框架用自然语言逐步解释预测的低频，并使用户能够通过自然语言反馈对个别步骤进行更正。我们专注于知识库问答(KBQA)作为框架的实例化，旨在增加句法分析过程的透明度，并帮助用户信任最终答案。我们构建了一个源自ComplexWebQuestions数据集的Inspirated，一个众包对话数据集。我们的实验表明，该框架具有极大地提高整体解析准确率的潜力。此外，我们还开发了一个对话模拟流水线来评估我们的框架w.r.t.各种最先进的KBQA模型，无需进一步的众包工作。结果表明，我们的框架承诺在这些模型上是有效的。
Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) in one turn. However, because natural language may contain ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user trust the final answer. We construct INSPIRED, a crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset. Our experiments show that this framework has the potential to greatly improve overall parse accuracy. Furthermore, we develop a pipeline for dialogue simulation to evaluate our framework w.r.t. a variety of state-of-the-art KBQA models without further crowdsourcing effort. The results demonstrate that our framework promises to be effective across such models.