Learning to Adapt to Low-Resource Paraphrase Generation (2022)

Abstract: Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse. At the same time, widely using large pre-trained language models (PLMs) faces the overfitting problem when training on scarce labeled data. To mitigate these two issues, we propose, LAPA, an effective adapter for PLMs optimized by meta-learning. LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data. This method enables paraphrase generation models to learn basic language knowledge first, then learn the paraphrasing task itself later, and finally adapt to the target task. Our experimental results demonstrate that LAPA achieves state-of-the-art in supervised, unsupervised, and low-resource settings on three benchmark datasets. With only 2% of trainable parameters and 1% labeled data of the target task, our approach can achieve a competitive performance with previous work.

## introduction paraphrase generation can comprehend a sentence and generate another with the same semantics but with variations in lexicon or syntax, which has various applications on downstream tasks including query rewriting @xcite , data augmentation @xcite and language model pre-training @xcite . conventional approaches @xcite model the paraphrase generation as a supervised encoding-decoding problem, inspired by machine translation systems. however, the success of these methods often relies on a large number of parallel paraphrases, whose collection is timeconsuming and requires a lot of domain knowledge. therefore, in real scenarios with a small amount of parallel data, the model suffers from performance drops facing domain gaps. this phenomenon, known as domain shift problem @xcite , comes from the representation gap between training and testing domains with different writing styles or forms. to tackle this problem, unsupervised methods such as editing-based approaches @xcite or reinforcement learning @xcite , and weakly-supervised methods such as retrievalenhanced @xcite or prompt-based @xcite do not introduce or only introduce a small number of supervised signals, which limits their performance such that underperforms supervised methods. in fact, largescale unlabeled corpus data (ucd) and labeled source domain data (lsdd), as well as a few labeled target domain data (ltdd), can be easily achieved. therefore, we propose a new three-stage learning paradigm: pre-training, meta-learning, and fine-tuning, aiming to leverage the pre-trained knowledge on ucd, source domain knowledge on lsdd, and adapt to target domain on lsdd to improve the performance of low-resource paraphrase generation. in order to successfully implement this learning paradigm, we propose a simple yet effective model which combined pre-trained language model (plm) and maml @xcite , named learning to adapt to low-resource paraphrase generation (lapa). specifically, before meta-learning, we insert an adapter layer into each transformer layer of plm. an adapter layer is composed of a few parameters of feedforward layer and residual connection. during meta-training and fine-tuning, only the adapter layer and normalization layer are trainable. parameter freezing and residual connection can retain the prior knowledge of plm to avoid negative transfer effects. smaller-scale parameter updating can prevent maml from gradient explosion or diminishing problems when the number of maml inner loop iterations and model depth increase @xcite or training data is extremely scarce. overall, we hold the idea that paraphrasing is a fundamental ability of human beings. the paraphrase model should not rely on domain and seen data. therefore, we are committed to characterizing the basic ability of the paraphrase model, obtaining gains from each domain, and applying it to a specific domain. our contributions are summarized as follows: ## related work while the paraphrase generation performance is greatly improved with various supervised techniques @xcite @xcite @xcite , there are few studies regarding the lowresource setting. west et al. (2021) and meng et al. (2021) proposed novel unsupervised paraphrasing strategies by data augmentation based on reflective decoding or diverse decoding. ding et al. (2021) and yin et al. (2022) achieved improvements on various low-resource datasets with retrieved data and meta reinforcement learning. however, these studies only use a single large corpus for training the full plm, which suffers from domainshifting problems @xcite . besides, under the extreme low-resource setting, directly fine-tuning the full plm will cause an over-fitting problem @xcite . meta-learning helps improve low-resource performance in various recent studies, such as image classification @xcite , vehicle tracking @xcite and natural language processing @xcite @xcite . @xcite proposed a meta learner named maml, which uses other example tasks to learn how to effectively initialize a basic learner, which can be quickly generalized to new tasks. adapter modules have been mainly used for parameter-efficient and quick fine-tuning of a basic plms to new tasks @xcite @xcite @xcite . our paper proposes to incorporate meta-learning approaches to realize multi-domain migration and task adapter to realize parameter effective transfer learning (i.e., limited trainable parameters) to mitigate the above problems of paraphrase generation. ## experimental results how does a pencil and a liquid eyeliner differ? how come there's no physical evidence for sea dragons existing if they're the largestanimal in the sea. ## conclusion in this work, we investigate the problem of paraphrase generation under the low-resource setting and propose a simple yet effective approach lapa. we effectively combine transfer learning and meta-learning by using adapter modules as the bridge. whether in supervised, unsupervised or low-resource setting, the results that our approach achieves the sota results on benchmark datasets. in the future, we plan to explore how to choose a smaller but suitable high-quality source corpus for learning in the source domain to improve the effect of transferring to the target domain, because not all source domain data has a positive effect. second, we plan to extend this framework to other ai fields to solve low-resource problems in other scenarios and enable more industrial applications.