Abstract
Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generation, there has been a growing interest in generation from a discourse coherence perspective.However, existing lexical or semantic metrics such as BLEU, ROUGE, BertScore cannot effectively capture the discourse coherence.The development of discourse-specific automatic evaluation methods for assessing the output of LLMs warrants greater focus and exploration. In this paper, we present a novel automatic metric designed to quantify the discourse divergence between two long-form articles.Extensive experiments on three datasets from representative domains demonstrate that our metric aligns more closely with human preferences and GPT-4 coherence evaluation, outperforming existing evaluation methods.- Anthology ID:
- 2024.naacl-short.9
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 92–100
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.9
- DOI:
- Cite (ACL):
- Yinhong Liu, Yixuan Su, Ehsan Shareghi, and Nigel Collier. 2024. Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 92–100, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence (Liu et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.naacl-short.9.pdf