E m R el: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022)

Abstract: Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples.We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.

## introduction relation extraction aims at discovering structured knowledge in the form @xmath0. it is an essential task towards constructing knowledge bases. although a lot of efforts have been made in building advanced relation extraction systems, it is still a challenging problem under certain practical scenarios where multiple entities and relations are involved, e.g., document-level relation extraction @xcite and joint entity and relation extraction @xcite . existing works mostly take the entity perspective that focuses on exploring cross-entity interactions @xcite . they either treat relations as atomic labels specified in a final classifier @xcite @xcite , or simply search subjects and objects for each individual relation @xcite . however, as an essential component, relations also interact with entities and context, which jointly exhibit informative inter-triple correlations. e.g., the two relations capital of and located at often co-occur between the same pair of entities but with different probabilities conditioned on specific contextual clues. as a consequence, the capability to model and make full use of rich interactions across relations, entities, and context is crucial for the task. in this paper, we advocate a novel joint triple perspective for relation extraction (see figure 1 for illustration). different from previous works that only seek to represent entities, we propose emrel that creates, refines and leverages the embedded representations of relations. specifically, we first explicitly create relation representations as embedded vectors; then refine these relation (as well as entity) representations by modeling rich relationentity-context interactions via an attention-based fusion module; and finally identify valid triples by aligning the representation of entities and relations in a joint space, with a novel alignment function based on tucker decomposition. this joint triple perspective actually considers entities along with relations as components of a small, incontext knowledge graph, and completes this graph by aligning and reasoning to extract multiple valid triples. to demonstrate the advantage of the proposed emrel framework, we conduct experiments on two specific scenarios of multi-triple extraction: document-level relation extraction(re) and joint entity and relation extraction, with three popular datasets including docred @xcite , nyt @xcite and webnlg @xcite . the results verify the superiority of the joint triple perspective over the traditional entity perspective in multi-triple extraction. we also provide further ablation study to show the effectiveness of our fusion module and alignment function. joint entity and relation extraction joint entity and relation extraction is a popular task that extracts multi-triples along with their entities. existing works can be concluded into two frameworks: one that searches subjects and objects for each individual relation @xcite @xcite , and the other that directly see each word as a candidate entity and assign them with relation labels @xcite . both formulations do not explicitly include inter-triple correlations. very recently, @xcite propose to model the interdependencies between entity labels and relation labels. however, such correlation is constrained within a specific word position, while emrel exploits the global correlations among all triples and across entities, context, and relations. @xcite introduces a translation-based function that predicts object from subject and relation, while emrel proposes a more expressive alignment function that models the ternary interaction of subject, relation and object. relation embedding there is one specific previous work @xcite ) that also considers modeling relation representations. cre uses the sentence representation as relation embeddings, and scores them with the entity embeddings trained along with the knowledge base. this raises significant differences with emrel in both 1) technical design, emrel constructs and models independent relation representations that are not inherited from specific context, and 2) task settings, cre requires all entities be aligned to an existing knowledge base to train their embedding. ## conclusion in this paper, we propose emrel for multi-triple extraction. distinguished from existing works, em-rel explicitly creates, refines, and leverages the embedded representation of relations. notably, we design a novel alignment function that discriminates triple validity by aligning its components in a joint representation space. we conduct experiments on both document-level relation extraction and joint entity and relation extraction, to demonstrate the advantage of emrel over its baselines. emrel also provides a new joint triple perspective, where multi-triple extraction is formulated as completion of a small, context-dependent knowledge graph, with candidate entities and relations as its components. in the future, we think more intricate techniques e.g., graph-based reasoning, can be explored following such formulation.