Direct dependency parsing of the speech signal -- as opposed to parsing
speech transcriptions -- has recently been proposed as a task (Pupier et al.
2022), as a way of incorporating prosodic information in the parsing system and
bypassing the limitations of a pipeline approach that would consist of using
first an Automatic Speech Recognition (ASR) system and then a syntactic parser.
In this article, we report on a set of experiments aiming at assessing the
performance of two parsing paradigms (graph-based parsing and sequence labeling
based parsing) on speech parsing. We perform this evaluation on a large
treebank of spoken French, featuring realistic spontaneous conversations. Our
findings show that (i) the graph based approach obtain better results across
the board (ii) parsing directly from speech outperforms a pipeline approach,
despite having 30% fewer parameters.

直接依赖语音信号的句法分析被提出作为一种任务，该任务旨在将韵律信息纳入分析系统并绕过使用自动语音识别系统和句法分析器的管道方法的局限性。本文报告了一系列实验，旨在评估两种句法分析范式（基于图的分析和基于序列标注的分析）在语音分析中的性能。我们在一大型的法语口语树库上进行了评估，其中包含真实的自发对话。我们的研究结果表明，（i）基于图的方法在整体上获得了更好的结果，（ii）直接从语音中进行分析优于管道方法，尽管参数量少了 30%。

以声音为基础的树生成：评估端到端语音依存句法分析策略

Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency  Parsing of Speech

We present a graph-based Tree Adjoining Grammar (TAG) parser that uses
BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end
parser, which jointly performs supertagging, POS tagging, and parsing,
outperforms the previously reported best results by more than 2.2 LAS and UAS
points. The graph-based parsing architecture allows for global inference and
rich feature representations for TAG parsing, alleviating the fundamental
trade-off between transition-based and graph-based parsing systems. We also
demonstrate that the proposed parser achieves state-of-the-art performance in
the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and
Unbounded Dependency Recovery. This provides further support for the claim that
TAG is a viable formalism for problems that require rich structural analysis of
sentences.

本文介绍了基于图形的 Tree Adjoining Grammar (TAG) 解析器，并使用 BiLSTMs、Highway 连接和字符级 CNNs 来实现，该解析器在全局推理和特征表示方面具有先进性，在短语结构随从词性标注和解析方面性能显著优于以往的结果，并且在解析评估和无界依赖恢复方面实现了最先进的性能。