Llamipa: An Incremental Discourse Parser
<|reference_start|>Llamipa: An Incremental Discourse Parser: 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.<|reference_end|>
@article{thompson2024llamipa:,
    title={Llamipa: An Incremental Discourse Parser},
    author={Kate Thompson, Akshay Chaturvedi, Julie Hunter, Nicholas Asher},
    journal={arXiv preprint arXiv:2406.18256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18256},
    primaryClass={cs.CL}
}