S elf ORE : Self-supervised Relational Feature Learning for Open Relation Extraction (2020)

Abstract: Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.

## introduction with huge amounts of information people generate, relation extraction (re) aims to extract triplets of the form (subject, relation, object) from sentences, discovering the semantic relation that holds between two entities mentioned in the text. existing re methods work well on pre-defined relations that have already appeared either in human-annotated datasets or knowledge bases. while in practice, human annotation can be laborintensive to obtain and hard to scale up to a large 1 https://github.com/thu-bpm/selfore † corresponding authors. number of relations. lots of efforts are made to alleviate the human annotation efforts in relation extraction. distant supervision @xcite is a widely-used method to train a supervised relation extraction model with less annotation as it only requires a small amount of annotated triplets as the supervision. however, distant supervised methods usually make strong assumptions on entity cooccurrence without sufficient contexts, which leads to noises and sparse matching results. more importantly, it works on a set of pre-defined relations, which prevents its applicability on open-domain text corpora. open relation extraction (openre) aims at inferring and extracting triplets where the target relations cannot be specified in advance. besides approaches that first identify relational phrases from open-domain corpora using heuristics or external labels via distant supervision and then recognize entity pairs @xcite , clustering-based unsupervised representation learning models get lots of attentions recently due to their ability to recognize triplets from meaningful semantic features with minimized or even no human annotation. @xcite regards openre as a totally unsupervised task and uses clustering method to extract triplets with new relation types. however, it cannot effectively discard irrelevant information and select meaningful relations. @xcite al. (2019) to further alleviate the human annotation efforts while obtaining high-quality supervision for open relation extraction, in this paper, we propose a selfsupervised learning framework which obtains supervision from the data itself and learns to improve the supervision quality by learning better feature presentations in an iterative fashion. the proposed ## proposed model the proposed model selfore consists of three modules: contextualized relation encoder, adap-tive clustering, and relation classification. as illustrated in figure 1 , the contextualized relation encoder takes sentences as the input, where named entities are recognized and marked in the sentence. contextualized relation encoder leverages the pretrained bert @xcite model to output contextualized entity pair representation. the adaptive clustering takes the contextualized entity pair representation as the input, aiming to perform clustering that determines the relational cluster an entity pair belongs to. unlike traditional clustering methods which assign hard cluster labels to each entity pair and are sensitive to the number of clusters, adaptive clustering performs soft-assignment which encourages high confidence assignments and is insensitive to the number of clusters. the pseudo labels based on the clustering results are considered as the self-supervised prior knowledge, which further guides the relation classification and features learning in contextualized relation encoder. before introducing details of each module, we briefly summarize the overall learning schema: ĩ 1 obtain contextualized entity pair representations based on entities mentioned in sentences using contextualized relation encoder. ĩ 2 apply adaptive clustering based on updated entity pair representations in 1 to generate pseudo labels for all relational entity pairs. ĩ 3 use pseudo labels as the supervision to train and update both contextualized relation encoder and relation classification. repeat 2 . ## experiments we conduct extensive experiments on real-world datasets to show the effectiveness of our selfsupervised learning rationale on relation extraction, and give a detailed analysis to show its advantages. ## related works relation extraction focuses on identifying the relation between two entities in a given sentence. traditional closed-domain relation extraction methods are supervised models. they need a set of predefined relation labels and require large amounts of annotated triplets, making them less ideal to work on open-domain corpora. distant supervision @xcite is a widely used method to alleviate human annotation: if multiple sentences contain two entities that have a certain relation in a knowledge base, at least one sentence is believed to convey the related relation. however, entities convey semantic meanings also according to the contexts, distant supervised models do not explicitly consider contexts and the models cannot discover new relations as the supervision is purely adopted from knowledge bases. unsupervised relation extraction @xcite @xcite gets lots of attention, due to the ability to discover relational knowledge without access to annotations and external resources. unsupervised models either 1) cluster the relation representation extracted from the sentence; 2) make more assumptions that provide learning signals for classification models. among clustering models, an important milestone is the openie approach @xcite , assuming the surface form of relations will appear be-tween two entities in its dependency tree. however, these works heavily rely on surface-form relation and have less ideal generalization capabilities. to solve this problem, @xcite proposes a system that learns to supervise unsupervised openie model, which combines the strength and avoids the weakness in each individual openie system. relation knowledge transfer system @xcite learns similarity metrics of relations from labeled data, and then transfers the relational knowledge to identify novel relations in unlabeled data. @xcite proposes a variational autoencoder approach (vae): the encoder part extracts relations from labeled features, and the decoder part predicts one entity when given the other entity and the relation with the function of triplet scoring @xcite . this scoring function could provide a signal since it is known to predict relation triplets when given their embeddings. however, posterior distribution and prior uniform distribution based on kl divergence is unstable. @xcite proposes a model to solve instability and trains the features on classifiers such as pcnn model @xcite . inspired by the success of self-supervised learning in computer vision @xcite , and large pretrained language models that show great potential in encoding meaningful semantics for various downstream tasks @xcite , we proposed a self-supervised learning schema for open-domain relation extraction. it has the advantages of unsupervised learning to handle the cases where the number of relations is not known in advance, but also keeps the advantage of supervised learning that has strong discriminative power for relational feature learning.