Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915214037876736 |
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| author | Yung, Frances Suresh, Varsha Reza, Zaynab Ahmad, Mansoor Demberg, Vera |
| author_facet | Yung, Frances Suresh, Varsha Reza, Zaynab Ahmad, Mansoor Demberg, Vera |
| contents | Implicit discourse relation recognition (IDRR) -- the task of identifying the implicit coherence relation between two text spans -- requires deep semantic understanding. Recent studies have shown that zero- or few-shot approaches significantly lag behind supervised models, but LLMs may be useful for synthetic data augmentation, where LLMs generate a second argument following a specified coherence relation. We applied this approach in a cross-domain setting, generating discourse continuations using unlabelled target-domain data to adapt a base model which was trained on source-domain labelled data. Evaluations conducted on a large-scale test set revealed that different variations of the approach did not result in any significant improvements. We conclude that LLMs often fail to generate useful samples for IDRR, and emphasize the importance of considering both statistical significance and comparability when evaluating IDRR models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition Yung, Frances Suresh, Varsha Reza, Zaynab Ahmad, Mansoor Demberg, Vera Computation and Language Implicit discourse relation recognition (IDRR) -- the task of identifying the implicit coherence relation between two text spans -- requires deep semantic understanding. Recent studies have shown that zero- or few-shot approaches significantly lag behind supervised models, but LLMs may be useful for synthetic data augmentation, where LLMs generate a second argument following a specified coherence relation. We applied this approach in a cross-domain setting, generating discourse continuations using unlabelled target-domain data to adapt a base model which was trained on source-domain labelled data. Evaluations conducted on a large-scale test set revealed that different variations of the approach did not result in any significant improvements. We conclude that LLMs often fail to generate useful samples for IDRR, and emphasize the importance of considering both statistical significance and comparability when evaluating IDRR models. |
| title | Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.20588 |