论文标题
与细心的图形卷积网络相结合的语法
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks
论文作者
论文摘要
Supertaging通常被认为是组合类别语法（CCG）解析的重要任务，在这种情况下，有效的上下文信息建模对此任务非常重要。但是，现有的研究做出了有限的努力来利用上下文特征，除了应用强大的编码器（例如BI-LSTM）。在本文中，我们提出了细心的图形卷积网络，以通过利用上下文信息的新颖解决方案来增强神经CCG超库。具体而言，我们从词典中提取的块（n-gram）构建图形，并在图表上应用注意力，以便在模型中加权块内和跨块的上下文的不同单词对并相应地促进超级词。在CCGBANK上进行的实验表明，我们的方法在超级绘制和解析方面都优于所有先前的研究。进一步的分析说明了每个组件在我们的方法中的有效性，以从单词对学习以增强CCG超构累。
Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information. Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly. The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing. Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.
