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Paraphrase generation is a difficult problem. This is not only because of thelimitations in text generation capabilities but also due that to the lack of aproper definition of what qualifies as a paraphrase and corresponding metricsto measure how good it is. Metrics for evaluation of paraphrasing quality is anon going research problem. Most of the existing metrics in use having beenborrowed from other tasks do not capture the complete essence of a goodparaphrase, and often fail at borderline-cases. In this work, we propose anovel metric $ROUGE_P$ to measure the quality of paraphrases along thedimensions of adequacy, novelty and fluency. We also provide empirical evidenceto show that the current natural language generation metrics are insufficientto measure these desired properties of a good paraphrase. We look at paraphrasemodel fine-tuning and generation from the lens of metrics to gain a deeperunderstanding of what it takes to generate and evaluate a good paraphrase.

Understanding Metrics for Paraphrasing