## 1 Introduction  
One of the most important information extraction tasks in natural language processing is extracting entities and relations from unstructured text to generate structured relational triples. Typically, the structured triple takes the form (subject, relation, object), where both the subject and the object are entities tied together by a relation. Extracting the triple of structured knowledge from an unstructured text can serve downstream tasks such as knowledge graphs [1, 2], question answering [3, 4], and biomedical tasks [5, 6]. Early studies [7, 8, 9, 10] on relational triple extraction split the work into two parts, identifying entities [11, 12] and predicting their relations [7, 13]. Specifically, the first step is identifying all entities in the text, then enumerating entities for relation judgement. This method is known as the pipeline-based approach. The advantage of this strategy is that it may leverage existing techniques to identify the named entity and classify the relations. However, there are two drawbacks as follows: (1) the link between entity identification and relation prediction is disregarded, and (2) errors in one subtask can easily propagate to other tasks. To solve the above shortcomings, joint extraction [14, 15, 16], which is the simultaneous extraction of entities and relationships, is studied. CasRel [17] identifies all the subjects and then identifies the relations and objects related to the subjects. TDEER [18] extracts the possible relations and subjects, respectively, then decodes the objects using an attention mechanism. PRGC [19] first identifies the relation and entity pairs and then aligns them. These sequence labeling approaches make use of multiple tagging sequence networks to determine the beginning and ending positions of entities. Although the above methods have achieved a promising performance, they have a common feature in that they extract subjects before objects and relations or relations before entities. This method has a fatal drawback in that the subject extraction failure will lead to the entire triple being impossible to extract. Specifically, as shown in Figure 1 for the triple _([PERSON], Nationality, China)_, failure to extract the subject _[PERSON]_ will lead to the failure to extract the object _China_ and relation _Nationality_. The extraction of triples is sensitive to whether the subject is extracted, a problem that exists in most current methods. The problem is detrimental to the identification of entity pairs; furthermore, it significantly impairs the performance of the whole relational triple-extraction task. In this paper, to mitigate error propagation, we designed a bidirectional framework to identify entity pairs. We gave the subject and object equal importance in this framework, which extracts entity pairs using forward extraction (subject \(\rightarrow\) entity pair, denoted as _s2p_) and backward extraction (object \(\rightarrow\) entity pair, denoted as _o2p_), respectively. Meanwhile, the features in both directions flow into each other, which enhances the convergence speed and recognition accuracy. Secondly, to achieve the efficient and accurate alignment of entity pairs, a translating decoding approach is presented based on the attention mechanism. This approach decodes entity pairs by translating relational features and the results of entity recognition. Finally, to fully mine the relations between entity pairs, we add relational features to the bidirectional decoding to strengthen the interaction between entity pairs and relations. We also design a (entity pair, relation) bipartite graph, which maintains a relation matrix for each relation, thus effectively implementing a mapping of entity pairs to relationships. We implemented the above approach in BTDM. This consists of an encoder module, a bidirectional entity, and alignment of the subject-object module and relation judgement module.