S tereo R el: Relational Triple Extraction from a Stereoscopic Perspective (2021)

Abstract: Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.

## introduction relational triple is a common structural representation of semantic facts. a triple is always in form of @xcite , where subject and object are two entities connected by a type of predefined semantic relation. relational triple extraction from unstructured texts is critical to understanding massive text corpora and constructing largescale knowledge graph @xcite , which is widely concerned in recent years. early researches @xcite @xcite first recognize entities and predict the relations for each entity pair. such approaches suffer from error propagation problem and thus recent researches @xcite @xcite @xcite try to build a jointly-decoding schema for entities and relations. however, relational triple extraction still faces the following challenging issues: to intuitively explore the above issues and address them, from a stereoscopic perspective, we map the relational triples of a text to a threedimensional (3-d) space, which is like a cube as figure 1 . the relational triples are actually some small cubes in the whole cube. existing researches are actually to model the cube from different perspectives and further extract the triples. based on the representation of triples in 3-d space, three operations (i.e. slice, projection and shrinkage) are defined as figure 2 , to understand why existing methods suffer from the above issues. furthermore, we propose a novel model for relational triple extraction, which can simultaneously handle the above issues, named stereorel. more precisely, the cube is modelled from three perspectives, including (x, z)-plane projection, (y, z)-plane projection and z-slices, which indicates the subjects, objects and their correspondences for each predefined relation. correspondingly, the proposed method leverages three decoders to extract relational triples in a unified model. this work has the following main contributions: ## relational triple extraction from 3-d perspective in form of (subject, relation, object), triples can naturally be mapped to a three-dimensional (3-d) space, which is elaborated in this section. meanwhile, we define three operations (i.e. slice, projection and shrinkage) in 3-d space, to make it easy to understand the strengths and shortcomings of previous researches. ## the proposed stereorel model to handle the above three issues simultaneously, we avoid to make the operations in ## discussions and perspectives for relational triple extraction, from the stereoscopic perspective, there are the following two aspects worthy of discussion. the first one is about learning strategy. most of previous studies and ours employ binary cross-entropy loss to learn the models. however, since the label space of relational triple in 3-d space is huge, binary cross-entropy is available but not necessarily optimal. meanwhile, cross-entropy is permutation-sensitive loss function @xcite , which is incompatible with generative models @xcite @xcite since it is necessary to predetermine extraction order of multiple triples. to this question, cgt @xcite incorporates contrastive learning strategy and spn @xcite transforms relational triple extraction into set prediction problem learned by bipartite matching loss. these ideas may be introduced in the future. the second one is to recognize nested entities in relational triples. nested entities are the entities among which there are substring relationships, like "u.n." being a substring of "u.n. ambassador". such entities definitely affect the overall performance on exact match mode. take nyt dataset as an example, there are about 2.5% sentences containing nested entities. nested entity recognition has been widely studied @xcite , but most studies on relational triple extraction have not considered it. tplinker @xcite provides a solution to recognize nested entities via a token pair tagging, but it ignores the interaction between entity and relation. for stereorel model, although not focusing on nested entities, it has not been much affected. the reason is that stereorel recognizes subjects and objects for each predefined relation separately. in this case, only 0.06% of nested entities in nyt cannot be marked. anyway, modeling nested entities from the stereoscopic perspective is worth exploring in the future. ## conclusions relational triple extraction is critical to understanding massive text corpora. however, existing studies face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. in this paper, aiming at simultaneously handling the above issues, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. further, we propose a novel model leveraging three decoders to respectively extract subjects, objects and their correspondences for each predefined relation. extensive experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.