Abstract
For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic similarity. We introduce methods based on sentence mover’s similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings. We find that sentence-based metrics correlate with human judgments significantly better than ROUGE, both on machine-generated summaries (average length of 3.4 sentences) and human-authored essays (average length of 7.5). We also show that sentence mover’s similarity can be used as a reward when learning a generation model via reinforcement learning; we present both automatic and human evaluations of summaries learned in this way, finding that our approach outperforms ROUGE.- Anthology ID:
- P19-1264
- Original:
- P19-1264v1
- Version 2:
- P19-1264v2
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2748–2760
- Language:
- URL:
- https://aclanthology.org/P19-1264
- DOI:
- 10.18653/v1/P19-1264
- Cite (ACL):
- Elizabeth Clark, Asli Celikyilmaz, and Noah A. Smith. 2019. Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2748–2760, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (Clark et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P19-1264.pdf