首页> 外文会议>Conference on empirical methods in natural language processing >Linguistic representations in multi-task neural networks for ellipsis resolution
【24h】

Linguistic representations in multi-task neural networks for ellipsis resolution

机译:省略省份分辨率多任务神经网络的语言表示

获取原文

摘要

Sluicing resolution is the task of identifying the antecedent to a question ellipsis. Antecedents are often sentential constituents, and previous work has therefore relied on syntactic parsing, together with complex linguistic features. A recent model instead used partial parsing as an auxiliary task in sequential neural network architectures to inject syntactic information. We explore the linguistic information being brought to bear by such networks, both by defining subsets of the data exhibiting relevant linguistic characteristics, and by examining the internal representations of the network. Both perspectives provide evidence for substantial linguistic knowledge being deployed by the neural networks.
机译:荡妇解析是将前任的任务识别为问题省略号。前者通常是句子组成部分,因此之前的工作依赖于句法解析,以及复杂的语言特征。最近的模型,而是将部分解析用作顺序神经网络架构中的辅助任务,以注入句法信息。我们通过定义具有相关语言特征的数据的子集,并通过检查网络的内部表示来探索这种网络的语言信息。这两种观点都为神经网络部署的主要语言知识提供了证据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号