论文标题
用于语义结构的无监督派生的凸层建模，用于数据有效的自然语言理解
Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding
论文作者
论文摘要
自然语言理解（NLU）的流行方法通常依赖大量的注释数据或手工制作的规则，这些规则艰苦，不适合域扩展。最近，我们提出了一个基于凸的基于模型的框架，该框架通过利用RAW对话框语料库来自动提取语义模式显示出巨大的潜力。提取的语义模式可用于生成语义帧，这对于协助NLU任务至关重要。本文进一步研究了CPM模型，并可以想象其在不同级别的高解释性和透明度。我们证明该框架可以利用 语料库中与语义框架相关的特征，揭示了话语的潜在语义结构，并以最少的监督提高了最先进的NLU模型的性能。我们在ATI（空中旅行信息系统）语料库上进行实验。
Popular approaches for Natural Language Understanding (NLU) usually rely on a huge amount of annotated data or handcrafted rules, which is laborious and not adaptive to domain extension. We recently proposed a Convex-Polytopic-Model-based framework that shows great potential in automatically extracting semantic patterns by exploiting the raw dialog corpus. The extracted semantic patterns can be used to generate semantic frames, which is essential in assisting NLU tasks. This paper further studies the CPM model in depth and visualizes its high interpretability and transparency at various levels. We show that this framework can exploit semantic-frame-related features in the corpus, reveal the underlying semantic structure of the utterances, and boost the performance of the state-of-the-art NLU model with minimal supervision. We conduct our experiments on the ATIS (Air Travel Information System) corpus.
