When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing
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Computer Science > Computation and Language
arXiv:2610.11520 (cs)
[Submitted on 8 Oct 2026]
Title:When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing
View a PDF of the paper titled When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing, by Minnie Kabra and 2 other authors
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Abstract:End-to-end speech parsing, a task recently proposed, consists in predicting both the transcription and the syntactic tree for a spoken utterance. Existing architectures for speech parsing often utilise intermediate neural networks. In this work, we examine the effectiveness of intermediate neural networks (NN) for parsing, and, specifically, what role do they play. We introduce a simpler end-to-end architecture for speech parsing, where we remove these intermediate NN units, reducing the parameters by 12%, while achieving comparable or better performance than prior method on both automatic speech recognition (ASR) and parsing. We demonstrate that intermediate NN units help reduce the representational gap when the pre-trained encoder is frozen. We do a comprehensive evaluation of speech parsing on French, and medium-low resource languages Slovenian and Naija. We further investigate the impact of the training data size and intermediate layers of the pretrained speech encoder on speech parsing.
| Comments: | to appear in Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11520 [cs.CL] |
| (or arXiv:2610.11520v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11520
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing, by Minnie Kabra and 2 other authors
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