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        <title>Sentence Mover‘s Similarity: Automatic Evaluation for Multi-Sentence Texts</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Sentence Mover‘s Similarity: Automatic Evaluation for Multi-Sentence Texts
%A Clark, Elizabeth
%A Celikyilmaz, Asli
%A Smith, Noah A.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F clark-etal-2019-sentence
%X 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.
%R 10.18653/v1/P19-1264
%U https://aclanthology.org/P19-1264/
%U https://doi.org/10.18653/v1/P19-1264
%P 2748-2760

Markdown (Informal)

[Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts](https://aclanthology.org/P19-1264/) (Clark et al., ACL 2019)

• Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (Clark et al., ACL 2019)

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.