EMNLP 2024finding3 citations
Llamipa: An Incremental Discourse Parser
Kate Thompson, Akshay Chaturvedi, Julie Hunter, Nicholas Asher
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.
BibTeX
@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"
}