Abstract
Automatic evaluation metrics are indispensable for evaluating generated text. To date, these metrics have focused almost exclusively on the content selection aspect of the system output, ignoring the linguistic quality aspect altogether. We bridge this gap by proposing GRUEN for evaluating Grammaticality, non-Redundancy, focUs, structure and coherENce of generated text. GRUEN utilizes a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. Unlike most existing evaluation metrics which require human references as an input, GRUEN is reference-less and requires only the system output. Besides, it has the advantage of being unsupervised, deterministic, and adaptable to various tasks. Experiments on seven datasets over four language generation tasks show that the proposed metric correlates highly with human judgments.- Anthology ID:
- 2020.findings-emnlp.9
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 94–108
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.9
- DOI:
- 10.18653/v1/2020.findings-emnlp.9
- Cite (ACL):
- Wanzheng Zhu and Suma Bhat. 2020. GRUEN for Evaluating Linguistic Quality of Generated Text. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 94–108, Online. Association for Computational Linguistics.
- Cite (Informal):
- GRUEN for Evaluating Linguistic Quality of Generated Text (Zhu & Bhat, Findings 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.findings-emnlp.9.pdf
- Code
- WanzhengZhu/GRUEN + additional community code
- Data
- CNN/Daily Mail, CoLA