DRK : Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction (2022)

Abstract: Few-shot relation extraction aims to identify the relation type between entities in a given text in the low-resource scenario. Albeit much progress, existing meta-learning methods still fall into prediction confusions owing to the limited inference ability over shallow text features. To relieve these confusions, this paper proposes a discriminative rule-based knowledge (DRK) method. Specifically, DRK adopts a logic-aware inference module to ease the word-overlap confusion, which introduces a logic rule to constrain the inference process, thereby avoiding the adverse effect of shallow text features. Also, DRK employs a discrimination finding module to alleviate the entity-type confusion, which explores distinguishable text features via a hierarchical contrastive learning. We conduct extensive experiments on four types of meta tasks and the results show promising improvements from DRK (6.0% accuracy gains on average). Besides, error analyses reveal the word-overlap and entity-type errors are the main courses of mispredictions in few-shot relation extraction.

## introduction with the emergence of new relation types and everincreasing annotation costs, traditional data-driven relation extraction (re) methods cannot survive in this low-data regime @xcite . therefore, the task of few-shot relation extraction is proposed to cope with such a lowresource dilemma. in few-shot re, meta-learning (ml) has been extensively employed and attained promising performance, the core of which is to learn the generalization ability from the data-rich classes to help predict the data-scarce classes @xcite . these ml approaches can be roughly divided into two categories: basic ml only accessible to raw sentence text, e.g., prototype @xcite and maml @xcite , and * * corresponding author. albeit much progress, the insufficient labeled data forces existing ml methods to make shallow inferences based on superficial text features, e.g., the word overlap @xcite and the matched entity type @xcite . in this light, as shown in fig. 1 , when the support instances of some relations exhibit massive word overlaps or have a matched entity-type pair, these ml methods inevitably get caught in the prediction confusions @xcite . based on the taxonomy of similar pattern, prediction confusions can be further classified into the word-overlap confusion and the entity-type confusion as shown in fig. 1 . for the word-overlap confusion, intuitively, incorporating external knowledge, e.g., relation descriptions @xcite , an entity-level knowledge graph (kg) @xcite or entity types @xcite @xcite , into re models can ease this semantic uncertainty to some extent. however, this simple incorporation (concatenation in the majority) just provides additional information, and cannot alter the fact that existing ml methods still process the shallow inferences. even worse, the lengthened text may exceed the input limit and introduce noise, thereby degrading the model performance. besides, the entity-type confusion can not be relieved with the introduction of external knowledge. since external information generally contains entity type information, the simple introduction cannot work in relations whose entity-type pairs are matched in head and tail positions. although the entity masking @xcite and some data augmentations @xcite can increase the relation separability by changing the data format, these solutions still rely on the superficial features and fail to grasp the subtle semantic differences. ## approaches in this section, we first introduce the task definition as well as the framework of drk in §3.1. then, we detail the logic-aware inference module in §3.2 and the discrimination finding module in §3.3. ## conclusion this paper focuses on the prediction confusions in few-shot re. to relieve these confusions, this paper develops a discriminative rule-based knowledge (drk) method consisting of a logic-aware inference module and a discrimination finding module. specifically, the first module relieves the wordoverlap confusion through the rule-based knowledge incorporation and the other module alleviates the entity-type confusion by a hierarchical contrastive learning. extensive experiments show the effectiveness of our proposal. as for future work, we plan to explore the confusion by the parameterefficient prompt tuning @xcite .