Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
Carnegie Mellon University（卡内基梅隆大学）；Google DeepMind（谷歌DeepMind）；University of Washington（华盛顿大学）
英文摘要：
We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates. Built using an extension to the segmental RNN that emphasizes recall, our basic system achieves competitive performance without any calls to a syntactic parser. We then introduce a method that uses phrase-syntactic annotations from the Penn Treebank during training only, through a multitask objective; no parsing is required at training or test time. This "syntactic scaffold" offers a cheaper alternative to traditional syntactic pipelining, and achieves state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
