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--- |
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license: apache-2.0 |
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language: |
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- zh |
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--- |
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# AS-SRL: A Chinese Speech-based Semantic Role Labeling Dataset |
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## Description |
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AS-SRL is the first Chinese speech-based Semantic Role Labeling (SRL) dataset, created by annotating the open-source Mandarin speech corpus AISHELL-1 with semantic role labels following the guidelines of Chinese Proposition Bank 1.0 (CPB1.0). The dataset contains 9,000 speech-text pairs with corresponding SRL annotations, split into training (7,500), development (500), and test (1,000) sets. |
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This dataset was developed to address the growing need for speech-based language understanding capabilities, particularly for SRL from speech input directly. It enables research on end-to-end approaches for SRL from speech, which can help overcome limitations of traditional pipeline methods (ASR followed by text-based SRL) such as error propagation and loss of useful acoustic features. |
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## Features |
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- 9,000 authentic Mandarin speech recordings with corresponding transcripts and SRL annotations |
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- 27,113 predicate-argument tuples across all splits |
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- 17 semantic role types including core arguments (ARG0-ARG4) and various modifiers (ARGM-*) |
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- High-quality annotations verified through a rigorous multi-annotator process with 86% inter-annotator agreement |
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## Citation |
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If you use this dataset, please cite: |
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@inproceedings{chen-etal-2024-semantic, |
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title = "Semantic Role Labeling from Chinese Speech via End-to-End Learning", |
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author = "Chen, Huiyao and Li, Xinxin and Zhang, Meishan and Zhang, Min", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL 2024", |
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year = "2024", |
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pages = "8898--8911" |
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} |
