Edit Distance Based Curriculum Learning for Paraphrase Generation (2021)

Abstract: Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation. In this study, we apply curriculum learning to paraphrase generation for the first time. Different from machine translation, paraphrase generation allows a certain level of discrepancy in semantics between source and target, which results in diverse transformations from lexical substitution to reordering of clauses. Hence, the difficulty of transformations requires considering both source and target contexts. Experiments on formality transfer using GYAFC showed that our curriculum learning with edit distance improves the quality of paraphrase generation. Additionally, the proposed method improves the quality of difficult samples, which was not possible for previous methods.

## introduction paraphrase generation is a task that transforms expressions of an input sentence while retaining its meaning. while there are various subtasks in paraphrase generation, formality transfer @xcite @xcite @xcite @xcite has been extensively studied. as paraphrase generation can be regarded as a machine translation task @xcite within the same language, the same models @xcite have been applied to a monolingual parallel corpus. recent studies @xcite have shown that curriculum learning @xcite achieves faster convergence and improved translation quality on neural machine translation. curriculum learning designs a training process starting from easy training samples and gradually proceeds to difficult training samples. in these previous studies, curriculum learning that uses source-side features, i.e., sentence length and word rarity, as a metric to determine the difficulty has improved the quality of translation. in this study, we adopt curriculum learning to the paraphrase generation task. paraphrasing allows a certain level of semantic divergence between source and target sentences. for example, some paraphrases only require just a small number of transformations as shown in to address this problem, we propose to use an edit distance between a paraphrased sentence pair as a difficulty metric that approximates necessary amounts of transformations. we evaluate our method on a formality transfer task using grammarly's yahoo answers formality corpus (gyafc) @xcite . the result of paraphrase generation from informal english to formal english confirmed the effectiveness of curriculum learning based on the edit distance. the detailed analysis revealed that the proposed method contributes to performance improvement in difficult samples regardless of the difficulty metrics, while sentence length and word rarity based methods degraded the performance. ## proposed method we approximate the difficulty of transformation in paraphrase generation as edit distance between a paraphrased sentence pair: ## experiment we evaluate the performance of edit-distance based curriculum learning on a style transfer task: paraphrase generation from informal english to formal english using gyafc foot_0 @xcite . ## summary and future work in this study, we applied the edit distance to curriculum learning for paraphrase generation. experiment results on an informal to formal style transfer task confirmed the effectiveness of our method, particularly for paraphrasing difficult sentences. curriculum learning can be applied to any task when reasonable metrics for task difficulty are available. transfer learning using a pre-trained model @xcite has significantly improved the performance of various natural language processing tasks. in transfer learning, fine-tuning samples similar to the ones in the pre-training corpus should be easier to learn. we plan to apply our edit-distance based curriculum learning to transfer learning.