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arXiv · 2107.08251

Generative Pretraining for Paraphrase Evaluation

Jack Weston·Raphael Lenain·Udeepa Meepegama·Emil Fristed
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Abstract

We introduce ParaBLEU, a paraphrase representation learning model and evaluation metric for text generation. Unlike previous approaches, ParaBLEU learns to understand paraphrasis using generative conditioning as a pretraining objective. ParaBLEU correlates more strongly with human judgements than existing metrics, obtaining new state-of-the-art results on the 2017 WMT Metrics Shared Task. We show that our model is robust to data scarcity, exceeding previous state-of-the-art performance using only $50\%$ of the available training data and surpassing BLEU, ROUGE and METEOR with only $40$ labelled examples. Finally, we demonstrate that ParaBLEU can be used to conditionally generate novel paraphrases from a single demonstration, which we use to confirm our hypothesis that it learns abstract, generalized paraphrase representations.

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Jack Weston, Raphael Lenain, Udeepa Meepegama, Emil Fristed. 2021-07-24. Generative Pretraining for Paraphrase Evaluation. https://arxiv.org/abs/2107.08251

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