RFBFN : A Relation-First Blank Filling Network for Joint Relational Triple Extraction (2022)

Abstract: Joint relational triple extraction from unstructured text is an important task in information extraction. However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially. To address the issues, we introduce a new blank filling paradigm for the task, and propose a relation-first blank filling network (RFBFN). Specifically, we first detect potential relations maintained in the text to aid the following entity pair extraction. Then, we transform relations into relation templates with blanks which contain the fine-grained semantic representation of the relations. Finally, corresponding subjects and objects are extracted simultaneously by filling the blanks. We evaluate the proposed model on public benchmark datasets. Experimental results show our model outperforms current state-of-the-art methods. The source code of our work is available at: https://github.com/lizhe2016/RFBFN .

## introduction extracting pairs of entities with semantic relations from unstructured texts is essential in knowledge graph construction. given a text, the aim of this task is to detect triples, i.e., in the form of (subject, relation, object) or (s, r, o). traditional pipeline methods @xcite first extract entity mentions and then perform relation classification for each entity pair. however, they suffer from error propagation and ignore the interaction between the two tasks. different from the pipeline methods, joint learning methods @xcite @xcite aim to extract entities and relations simultaneously in an end-to-end way, which achieve promising performance. they tend to decompose the task into several subtasks and solve although previous works have achieved great success, the semantic information of relations is still underutilized. most models @xcite @xcite treat the relation extraction as a classification task which only replace the relation with a meaningless class id. to better capture the semantic information, machine reading comprehension (mrc) models @xcite @xcite are proposed to address the extraction task. @xcite and @xcite transform the task into a multi-turn question answering problem. the subjects are detected first by answering entity-specific questions. then, relationspecific questions are generated to extract objects. however, they predict subjects and objects sequentially and separately, and thus question answering is required to perform for multiple turns. more recently, the relation-first methods have shown promising performance in relational triple extraction @xcite , which benefit from the fact that relations are usually triggered by the context rather than entities. for example, the "creator" relation will be directly detected from descriptions such as "was created by". by predicting relations first, irrelevant relations are filtered out, which mitigates negative effects caused by useless relations and avoids the data imbalance issue. however, the subject-object two leaders of italy, where amatriciana sauce is found, are matteo renzi and sergio mattarella. ## related work early works @xcite @xcite treat the extraction as a pipeline of two separate tasks: an entity model first identifies entities and then a relation model extracts the relations between the entity mentions. however, these methods ignore the correlation between the two steps and suffer from the error propagation issue. to overcome these shortcomings, joint models @xcite ) are proposed, which can extract entities and relations simultaneously. traditional joint methods @xcite @xcite are feature-based and heavily rely on feature engineering, which require intensive manual efforts. to reduce manual work, recent studies have investigated neural network models, which include sequence tagging methods @xcite @xcite , sequence-to-sequence methods @xcite and table-filling methods @xcite . although above models make great progress, they still only treat the relation type as a meaningless class id or a trainable embedding @xcite which is not enough to capture the fine-grained semantic information of a relation. current works cast the task into a question answering problem with machine reading models. @xcite perform unsupervised relation extraction without a fine-tuned extractive head. however, they only extract objects from the given contexts and subjects. to joint extract entities and relations, @xcite ; @xcite first predict subjects from the context by answering entity questions. then, the extracted subjects are inserted to the slots to generate the relation ques- entity pairs × 𝑳 𝟐 . . . . . . relation detection module blank filling module triples relations 𝑯 𝒆 𝒓𝒆𝒍 𝑯 𝒆 𝒆𝒏𝒕 𝒙 𝒄𝒍s 𝒆 figure 2: the overall architecture of rfbfn. given a sentence x, rfbfn first predicts a subset of candidate relations in the relation detection module. then for each candidate relation, corresponding entity pairs are extracted by filling the blanks of the transformed relation templates in the blank filling module. q 1 , q 2 , ..., q 5 are learnable embeddings to predict relations. l 1 and l 2 are the numbers of the decoder blocks. in this paper, we propose a joint relation-first blank filling network to extract triples. different from previous works, we transform relations to specific relation templates to make use of semantic information of the relations. moreover, we extract subjects and objects at the same time in a nonautoregressive decoder without aligning them. ## conclusion in this paper, we design a novel blank filling paradigm for relational triple extraction, and present a relation-first blank filling network. we transform relations into relation templates with blanks to fill which can capture important semantic information of the relations. meanwhile, subjects and objects are extracted simultaneously by filling the blanks in the non-autoregressive decoder. to the best of our knowledge, we are the first to cast relational triple extraction as a blank filling problem, which may motivate new ideas and inspire future research directions. the experiment results on public datasets show that our model achieves state-of-the-art performance.