Rationale-Enhanced Language Models are Better Continual Relation Learners (2023)

Abstract: Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model’s lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by Large Language Models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.

## introduction relation extraction (re) aims to identify the relations between two entities in a text. while traditional re models cannot handle the real-life situation where new relations are constantly emerging, continual relation extraction (cre) attempts to learn new relations while retaining the performance on learned relations @xcite @xcite . similar to other continual learning tasks, the main challenge in cre is the phenomenon of catastrophic forgetting (cf), i.e., the performance on identifying old relations degrades significantly while learning new relations. most previous cre researches have attributed catastrophic forgetting to the destruction of representations learned on previous tasks when learning new tasks @xcite @xcite . they focused on recovering the representations on previous tasks, using methods like restricted gradient updating and knowledge distillation @xcite . recently, another line of work @xcite found that in cre scenario, models trained for the current task do not have good identification ability for new-coming relations which are analogous to a current relation. thus, making a model learn current relations robustly to avoid subsequent confusion becomes the new research focus @xcite . to address this problem, in this paper, we assume that incorporating rationales can enhance the performance of cre models in learning analogous relations. this is inspired by the intuition that, training models with explicit rationale supervision can provide greater robustness @xcite . moreover, since relation extraction requires reasoning over two entities, providing explanations for why the two entities have a specific relation can enhance the reasoning capacity of smaller models, ## task formalization in this work, we focus on continual learning for a sequence of n relation classification tasks {t 1 , t 2 , ..., t n }. for task t k , given a training set d @xmath0, y i ⟩|x @xmath1, y @xmath2, the model learns to identify the corresponding relation type y i for the input text x i , where y k denotes a set of new relation types and x k a set of text containing two entities. the goal of cre is to continually train the model on new tasks to learn new relations, while avoiding forgetting of previously learned ones. therefore, after learning n tasks, the model will be evaluated to identify each text x from test set into a relation type @xmath3. ## methodology figure 2 shows the model architecture of ra-tionalcl, which follows a two-stage training paradigm. in stage 1, we first make use of llm to generate rationales, with which the t5 model is trained under the multi-task rationale tuning framework to learn to identify new relations. then, to alleviate catastrophic forgetting in cre, we adopt an episodic memory module to store a few representative instances for each relation. the memory data will be continually learned with emerging new tasks. when selecting these representative instances for each relation, we follow @xcite and use the k-means algorithm to conduct clustering based on the features of all samples extracted by t5-encoder, and select the samples in the centre of each cluster into memory. in stage 2, we regenerate the contrastive rationale and conduct contrastive rationale replay under the multi-task framework to alleviate catastrophic forgetting. ## conclusion in this paper, we devise a framework by introducing rationales generated by llm into cre tasks to improve the model's robustness against future models tacred fewrel rationalecl 80.8 85.1 w/o con. replay 80.2 84.8 w/o taskd 80.0 84.6 w/o both 79.8 83.9