Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that supervised clarification models can rewrite fragmentary or underspecified utterances, such as resolving ellipsis or references, to improve parsing accuracy. In this work, we revisit this idea under realistic deployment conditions, where no clarification supervision is available and the clarifier must rely on zero-shot prompting or feedback from a frozen parser. Across three Segmented Discourse Representation Theory (SDR
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- Linked via arxiv authorYiming Liu →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorZiyue Zhang →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorZhichao Xu →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorXin Yu →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorYingheng Tang →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorTianyu Jiang →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
- Linked via arxiv authorJie Cao →
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing