Exploring Description-Augmented Dataless Intent Classification
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913445188730880 |
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| author | Hu, Ruoyu Khosmood, Foaad Edalat, Abbas |
| author_facet | Hu, Ruoyu Khosmood, Foaad Edalat, Abbas |
| contents | In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17862 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Description-Augmented Dataless Intent Classification Hu, Ruoyu Khosmood, Foaad Edalat, Abbas Computation and Language In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area. |
| title | Exploring Description-Augmented Dataless Intent Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.17862 |