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This paper investigates using LLMs to rewrite fragmentary dialogue utterances for improving frozen discourse parsers, finding that zero-shot clarification is unreliable and that error repair through rewriting has a practical ceiling, suggesting rewritability prediction as a key missing capability.
This paper introduces DraDDP, the first publicly available English multimodal dataset for multi-party dialogue discourse parsing, built from American TV dramas with 495 segments, 6,374 utterances, and 9.1 hours of video. Benchmarks show multimodal information improves parsing of dialogue structures and relation types.