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.

翻译：本文首次利用基于SDRT（分段篇章表征理论，Asher, 1993; Asher and Lascarides, 2003）风格标注语料微调的大语言模型（LLM）进行了篇章解析实验。由此产生的篇章解析器Llamipa（Llama增量解析器）能够有效利用篇章上下文，其性能显著优于采用编码器专用模型提供局部化、上下文敏感的篇章单元表征的方法。此外，该解析器能够以增量方式处理篇章数据，这对于在下游任务中最终利用篇章信息至关重要。