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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- LinkedLinked via arxiv author · 85%Yiming Liu →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Ziyue Zhang →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Zhichao Xu →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Xin Yu →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Yingheng Tang →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Tianyu Jiang →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- LinkedLinked via arxiv author · 85%Jie Cao →
“Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing”
- PossiblePossibly related (embedding) · 49%stanfordnlp/stanza →