论文标题
神经语义解析的迭代话语细分
Iterative Utterance Segmentation for Neural Semantic Parsing
论文作者
论文摘要
神经语义解析器通常无法将长时间和复杂的话语解析为正确的含义表示，因为缺乏利用构图原则。为了解决这个问题，我们提出了一个新颖的框架，用于通过迭代话语细分来促进神经语义解析器。给定输入话语，我们的框架在两个神经模块之间迭代：一种用于从说话分割跨度的分段，而分析器将跨度映射到部分含义表示表示。然后，这些中间解析结果组成了最终的含义表示。一个关键优势是，该框架不需要任何手工艺模板或其他标记的数据进行分割：我们通过提出一种新颖的培训方法来实现这一目标，在这种方法中，解析器为细分器提供了伪监督。关于GEO，复杂网络和公式的实验表明，我们的框架可以一致地改善不同领域中神经语义解析器的性能。在需要组成概括的数据拆分上，我们的框架带来了显着的准确性提高：GEO 63.1至81.2，公式59.7至72.7，复杂Webquestions 27.1至56.3。
Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input utterance, our framework iterates between two neural modules: a segmenter for segmenting a span from the utterance, and a parser for mapping the span into a partial meaning representation. Then, these intermediate parsing results are composed into the final meaning representation. One key advantage is that this framework does not require any handcraft templates or additional labeled data for utterance segmentation: we achieve this through proposing a novel training method, in which the parser provides pseudo supervision for the segmenter. Experiments on Geo, ComplexWebQuestions, and Formulas show that our framework can consistently improve performances of neural semantic parsers in different domains. On data splits that require compositional generalization, our framework brings significant accuracy gains: Geo 63.1 to 81.2, Formulas 59.7 to 72.7, ComplexWebQuestions 27.1 to 56.3.
