Learning Joint Semantic Parsers from Disjoint Data
north american chapter of the association for computational linguistics(2018)
摘要
We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap. We handle such "disjoint" data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络