Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
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DOI：
10.18653/v1/n18-2065
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发表时间：
2018-04
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影响因子：
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通讯作者：
Masashi Yoshikawa;K. Mineshima;Hiroshi Noji;D. Bekki
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文献类型：
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作者：
Masashi Yoshikawa;K. Mineshima;Hiroshi Noji;D. Bekki
在基于形式逻辑的文本蕴涵识别方法中，使用组合范畴语法（CCG）解析器来解析输入前提和假设以获得它们的逻辑公式。在这里，重要的是解析器处理句子的一致性;未能识别出相似的句法结构会导致它们之间的谓词论元结构不一致，在这种情况下，随后的定理证明注定要失败。在这项工作中，我们提出了一个简单的方法来扩展现有的CCG解析器来解析一组句子一致，这是实现与马尔可夫随机场（MRF）的句间建模。当与现有的基于逻辑的系统相结合，我们的方法总是在英语和日语的RTE实验显示出改善。
In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.