arXiv:2607.01964v1 Announce Type: new Abstract: 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 thre
This research comes as large language models (LLMs) mature, enabling more sophisticated zero-shot prompting and feedback mechanisms previously unavailable while addressing a known bottleneck in dialogue parsing.
This development allows for more robust and adaptable NLP pipelines by improving the performance of downstream models without requiring extensive, costly supervised clarification data, making LLM integration more practical.
The ability to effectively rewrite inputs for dialogue discourse parsing without explicit supervision means improved performance and broader application of conversational AI systems in real-world scenarios.
- · LLM developers
- · NLP platform providers
- · Conversational AI companies
- · SaaS providers leveraging dialogue systems
- · Companies relying on expensive, proprietary supervised NLP pipelines
- · Outdated dialogue parsing methodologies
Improved accuracy and efficiency of automated dialogue systems in various applications.
Reduced development costs and faster deployment cycles for advanced conversational AI.
Accelerated adoption of AI agents across white-collar workflows as foundational NLP becomes more robust and accessible.
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