Communication Dans Un Congrès Année : 2025

Structured Discourse Representation for Factual Consistency Verification

Résumé

Analysing the differences in how events are represented across texts, or verifying whether the language model generations hallucinate, requires the ability to systematically compare their content. To support such a comparison, a structured representation that captures fine-grained information plays a vital role. In particular, identifying distinct atomic facts and the discourse relations connecting them enables deeper semantic comparison. Our proposed approach combines structured discourse information extraction with a classifier, FDSpotter, for factual consistency verification. We show that adversarial discourse relations pose challenges for language models, but fine-tuning on our annotated data, DiscInfer, achieves competitive performance. Our proposed approach advances factual consistency verification by grounding in linguistic structure and decomposing it into interpretable components. We demonstrate the effectiveness of our method on the evaluation of two tasks: data-to-text generation and text summarisation. Our code and dataset will be publicly available on GitHub.

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Dates et versions

hal-05114343 , version 1 (16-06-2025)
hal-05114343 , version 2 (03-11-2025)

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  • HAL Id : hal-05114343 , version 1

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Kun Zhang, Oana Balalau, Ioana Manolescu. Structured Discourse Representation for Factual Consistency Verification. ACL 2025 - 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Jul 2025, Vienne, Austria. ⟨hal-05114343v1⟩
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