Revisiting the Evaluation Metrics of Paraphrase Generation

Paraphrase generation is an important NLP task that has achieved significant progress recently. However, one crucial problem is overlooked, `how to evaluate the quality of paraphrase?'. Most existing paraphrase generation models use reference-based metrics (e.g., BLEU) from neural machine translation (NMT) to evaluate their generated paraphrase. Such metrics' reliability is hardly evaluated, and they are only plausible when there exists a standard reference. Therefore, this paper first answers one fundamental question, `Are existing metrics reliable for paraphrase generation?'. We present two conclusions that disobey conventional wisdom in paraphrasing generation: (1) existing metrics poorly align with human annotation in system-level and segment-level paraphrase evaluation. (2) reference-free metrics outperform reference-based metrics, indicating that the standard references are unnecessary to evaluate the paraphrase's quality. Such empirical findings expose a lack of reliable automatic evaluation metrics. Therefore, this paper proposes BBScore, a reference-free metric that can reflect the generated paraphrase's quality. BBScore consists of two sub-metrics: S3C score and SelfBLEU, which correspond to two criteria for paraphrase evaluation: semantic preservation and diversity. By connecting two sub-metrics, BBScore significantly outperforms existing paraphrase evaluation metrics.