将超标签特征集成到神经非连续成分句法分析中
Integrating Supertag Features into Neural Discontinuous Constituent Parsing
AI总结：
本研究探索将超标签特征集成到基于转移的神经非连续成分句法分析中，通过流水线和多任务两种方式引入超标签信息，并对比多种框架与序列标注任务作为辅助任务的适用性。
摘要：
句法分析在自然语言处理中至关重要，成分结构是句法的一种广泛使用的描述。传统的成分观要求成分由相邻词组成，但这在分析具有非局部依赖的句法时带来了挑战，这种情况在德语等语言中很常见。因此，在德语的 NeGra 和 TIGER 以及英语的 DPTB 等树库中，长距离依赖通过交叉边来表示。已经使用了各种语法形式化来描述非连续树——通常具有很高的解析时间复杂度。基于转移的解析旨在通过消除对显式语法的需求来降低这一因素。相反，神经网络在大型标注语料库上使用监督学习进行训练，以在给定原始文本输入的情况下生成树。Coavoux 和 Cohen (2019) 开发的一种优雅的无栈基于转移的解析器提案，成功地允许在最坏情况下的二次时间内推导出句子上的任何非连续成分树。这项工作的目的是探索将超标签信息引入基于转移的非连续成分解析。在像 CCG (Steedman, 1989) 这样的词汇化语法形式化中，信息丰富的类别被分配给句子中的单词，并作为组成句子句法的构建块。这些超标签指示一个词的结构角色及其与周围项的句法关系。该研究通过使用专门的超标签器作为神经解析器（流水线）的额外输入，以及联合训练用于解析和超标签的神经模型（多任务）来检查超标签信息的整合。除了 CCG，还将比较其他几个框架（LTAG-spinal、LCFRS）和序列标注任务（分块、依存句法分析）作为解析辅助任务的适用性。
英文摘要：
Syntactic parsing is essential in natural-language processing, with constituent structure being one widely used description of syntax. Traditional views of constituency demand that constituents consist of adjacent words, but this poses challenges in analysing syntax with non-local dependencies, common in languages like German. Therefore, in a number of treebanks like NeGra and TIGER for German and DPTB for English, long-range dependencies are represented by crossing edges. Various grammar formalisms have been used to describe discontinuous trees - often with high time complexities for parsing. Transition-based parsing aims at reducing this factor by eliminating the need for an explicit grammar. Instead, neural networks are trained to produce trees given raw text input using supervised learning on large annotated corpora. An elegant proposal for a stack-free transition-based parser developed by Coavoux and Cohen (2019) successfully allows for the derivation of any discontinuous constituent tree over a sentence in worst-case quadratic time. The purpose of this work is to explore the introduction of supertag information into transition-based discontinuous constituent parsing. In lexicalised grammar formalisms like CCG (Steedman, 1989) informative categories are assigned to the words in a sentence and act as the building blocks for composing the sentence's syntax. These supertags indicate a word's structural role and syntactic relationship with surrounding items. The study examines incorporating supertag information by using a dedicated supertagger as additional input for a neural parser (pipeline) and by jointly training a neural model for both parsing and supertagging (multi-task). In addition to CCG, several other frameworks (LTAG-spinal, LCFRS) and sequence labelling tasks (chunking, dependency parsing) will be compared in terms of their suitability as auxiliary tasks for parsing.
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