Abstract
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level. We further introduce WPSLOR, a novel WordPiece-based version, which harnesses a more compact language model. Even though word-overlap metrics like ROUGE are computed with the help of hand-written references, our referenceless methods obtain a significantly higher correlation with human fluency scores on a benchmark dataset of compressed sentences. Finally, we present ROUGE-LM, a reference-based metric which is a natural extension of WPSLOR to the case of available references. We show that ROUGE-LM yields a significantly higher correlation with human judgments than all baseline metrics, including WPSLOR on its own.- Anthology ID:
- K18-1031
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Anna Korhonen, Ivan Titov
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 313–323
- Language:
- URL:
- https://aclanthology.org/K18-1031
- DOI:
- 10.18653/v1/K18-1031
- Cite (ACL):
- Katharina Kann, Sascha Rothe, and Katja Filippova. 2018. Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 313–323, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Sentence-Level Fluency Evaluation: References Help, But Can Be Spared! (Kann et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/K18-1031.pdf