Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech
<|reference_start|>Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech: 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.<|reference_end|>
@article{pupier2024growing,
    title={Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency
  Parsing of Speech},
    author={Adrien Pupier, Maximin Coavoux, J'er^ome Goulian, Benjamin Lecouteux},
    journal={arXiv preprint arXiv:2406.12621},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12621},
    primaryClass={cs.CL}
}