Llamipa: An Incremental Discourse Parser
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909333507276800 |
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| author | Thompson, Kate Chaturvedi, Akshay Hunter, Julie Asher, Nicholas |
| author_facet | Thompson, Kate Chaturvedi, Akshay Hunter, Julie Asher, Nicholas |
| contents | This paper provides the first discourse parsing experiments with a large language model(LLM) finetuned on corpora annotated in the style of SDRT (Segmented Discourse Representation Theory Asher, 1993; Asher and Lascarides, 2003). The result is a discourse parser, Llamipa (Llama Incremental Parser), that leverages discourse context, leading to substantial performance gains over approaches that use encoder-only models to provide local, context-sensitive representations of discourse units. Furthermore, it can process discourse data incrementally, which is essential for the eventual use of discourse information in downstream tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Llamipa: An Incremental Discourse Parser Thompson, Kate Chaturvedi, Akshay Hunter, Julie Asher, Nicholas Computation and Language This paper provides the first discourse parsing experiments with a large language model(LLM) finetuned on corpora annotated in the style of SDRT (Segmented Discourse Representation Theory Asher, 1993; Asher and Lascarides, 2003). The result is a discourse parser, Llamipa (Llama Incremental Parser), that leverages discourse context, leading to substantial performance gains over approaches that use encoder-only models to provide local, context-sensitive representations of discourse units. Furthermore, it can process discourse data incrementally, which is essential for the eventual use of discourse information in downstream tasks. |
| title | Llamipa: An Incremental Discourse Parser |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.18256 |