Dependency-aware Prototype Learning for Few-shot Relation Classification (2022)

Abstract: Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguish multiple relations that co-exist in one sentence. This paper presents a novel dependency-aware prototype learning (DAPL) method for few-shot relation classification. Concretely, we utilize dependency trees and shortest dependency paths (SDP) as structural information to complement the contextualized representations of input sentences by using the dependency-aware embedding as attention inputs to learn attentive sentence representations. In addition, we introduce a gate controlled update mechanism to update the dependency-aware representations according to the output of each network layer. Extensive experiments on the FewRel dataset show that DAPL achieves substantially better performance than strong baselines. For reproducibility, we will release our code and data upon the publication of this paper at https://github.com/publicstaticvo/DAPL .

## introduction relation classification, which aims to classify the relation between two entities in a sentence, is a fundamental task for information retrieval @xcite , knowledge graph construction @xcite and question answering @xcite . most of existing relation classification methods @xcite @xcite @xcite focus on the supervised scenario where sufficient labeled training data is available. however, it is time-consuming and labor-intensive to collect large-scale labeled data in many realworld applications, especially in the low-resource settings @xcite @xcite . ## methodology problem definition in the rc task, each instance consists of an input sequence x (including a input sentence z, a head entity e 1 , a tail entity e 2 ) and a relation label y for the two entities. we adopt a typical n -way-k-shot setting for fsrc @xcite . under n -way-k-shot configuration, the training data is further split into a support set s and a query set q which have disjoint labels, where s contains n relation classes and each with k labeled examples. the goal of fsrc is to learn a model using d train , which is then used to predict the relation y for each input x in testing set. ## conclusion in this paper, we proposed a dependency-aware prototype learning method for fsrc, which leveraged dependency trees and shortest dependency paths as structural information to complement the contextualized representations of input sentences. a gatecontrolled update mechanism was further devised to adaptively update the dependency-aware representations according to the output of each network layer. experimental results showed that dapl outperformed strong baselines for fsrc.