An Imitation Game for Learning Semantic Parsers from User Interaction
An Imitation Game for Learning Semantic Parsers from User Interaction
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DOI：
10.18653/v1/2020.emnlp-main.559
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Ziyu Yao;Yiqi Tang;Wen-tau Yih;Huan Sun;Yu Su
中科院分区：
文献类型：
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作者：
Ziyu Yao;Yiqi Tang;Wen-tau Yih;Huan Sun;Yu Su
尽管应用广泛成功，但引导和微调语义解析器仍然是一个乏味的过程，面临着昂贵的数据注释和隐私风险等挑战。在本文中，我们提出了一种替代的人机交互方法，用于直接向用户学习语义解析器。语义解析器应该反思其不确定性，并在不确定时提示用户演示。在此过程中，它还可以模仿用户行为并继续自主改进自己，希望最终它能像用户一样解释他们的问题。为了解决演示的稀疏性，我们提出了一种新颖的注释高效模仿学习算法，该算法通过混合演示状态和置信预测来迭代收集新数据集，并以数据集聚合方式重新训练语义解析器（Ross et al., 2011）。我们对其成本界限进行了理论分析，并凭经验证明了其在文本到 SQL 问题上的良好性能。
Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstration when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstration, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and re-trains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem.