SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913984287866880 |
|---|---|
| author | Yang, Zeyu Wei, Lai Koshkin, Roman Chen, Xi Nakamura, Satoshi |
| author_facet | Yang, Zeyu Wei, Lai Koshkin, Roman Chen, Xi Nakamura, Satoshi |
| contents | This work proposes a grammar-based chunking strategy that segments input streams into semantically complete units by parsing dependency relations (e.g., noun phrase boundaries, verb-object structures) and punctuation features. The method ensures chunk coherence and minimizes semantic fragmentation. Building on this mechanism, we present SASST (Syntax-Aware Simultaneous Speech Translation), an end-to-end framework integrating frozen Whisper encoder and decoder-only LLM. The unified architecture dynamically outputs translation tokens or <WAIT> symbols to jointly optimize translation timing and content, with target-side reordering addressing word-order divergence. Experiments on CoVoST2 multilingual corpus En-{De, Zh, Ja} demonstrate significant translation quality improvements across languages and validate the effectiveness of syntactic structures in LLM-driven SimulST systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation Yang, Zeyu Wei, Lai Koshkin, Roman Chen, Xi Nakamura, Satoshi Computation and Language This work proposes a grammar-based chunking strategy that segments input streams into semantically complete units by parsing dependency relations (e.g., noun phrase boundaries, verb-object structures) and punctuation features. The method ensures chunk coherence and minimizes semantic fragmentation. Building on this mechanism, we present SASST (Syntax-Aware Simultaneous Speech Translation), an end-to-end framework integrating frozen Whisper encoder and decoder-only LLM. The unified architecture dynamically outputs translation tokens or <WAIT> symbols to jointly optimize translation timing and content, with target-side reordering addressing word-order divergence. Experiments on CoVoST2 multilingual corpus En-{De, Zh, Ja} demonstrate significant translation quality improvements across languages and validate the effectiveness of syntactic structures in LLM-driven SimulST systems. |
| title | SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.07781 |