@inproceedings{thompson-etal-2024-llamipa,
title = "Llamipa: An Incremental Discourse Parser",
author = "Thompson, Kate and
Chaturvedi, Akshay and
Hunter, Julie and
Asher, Nicholas",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.373/",
doi = "10.18653/v1/2024.findings-emnlp.373",
pages = "6418--6430",
abstract = "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 is able to process discourse data incrementally, which is essential for the eventual use of discourse information in downstream tasks."
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<abstract>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 is able to process discourse data incrementally, which is essential for the eventual use of discourse information in downstream tasks.</abstract>
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%0 Conference Proceedings
%T Llamipa: An Incremental Discourse Parser
%A Thompson, Kate
%A Chaturvedi, Akshay
%A Hunter, Julie
%A Asher, Nicholas
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F thompson-etal-2024-llamipa
%X 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 is able to process discourse data incrementally, which is essential for the eventual use of discourse information in downstream tasks.
%R 10.18653/v1/2024.findings-emnlp.373
%U https://aclanthology.org/2024.findings-emnlp.373/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.373
%P 6418-6430
Markdown (Informal)
[Llamipa: An Incremental Discourse Parser](https://aclanthology.org/2024.findings-emnlp.373/) (Thompson et al., Findings 2024)
ACL
- Kate Thompson, Akshay Chaturvedi, Julie Hunter, and Nicholas Asher. 2024. Llamipa: An Incremental Discourse Parser. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6418–6430, Miami, Florida, USA. Association for Computational Linguistics.