TREND : Trigger-Enhanced Relation-Extraction Network for Dialogues (2022)

Abstract: The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called “triggers”. However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.

## introduction the goal of relation extraction (re) is to identify the semantic relation type between two mentioned entities from a given text piece, which is one of basic and important natural language understanding (nlu) problems @xcite @xcite . in this task setting, we are usually given a written sentence and a query pair containing two entities and asked to return the most possible relation type from a predefined set of relations. dialogue relation extraction (dre), on the other hand, aims to excavate underlying crosssentence relation in natural human communications @xcite . the problem itself is well-motivated, because relations between entities in dialogues could potentially provide dialogue systems with additional features for better dialogue managing @xcite or response generation @xcite . there are two popular datasets, dialogre @xcite and ddrel @xcite , focusing on relation extraction in dialogues illustrated in figure 1 . in dre, given a conversation and a query pair, we aim to identify the interpersonal relationship between the given entities, where entities can be human or other types like locations. as shown in figure 1 , the evidences of relations within the conversation flow, called triggers, provide informative cues for this task. a trigger can be a short phrase or even a single word with any possible part-of-speech. in the example, the clue for knowing the speaker 2 has a negative impression on the speaker 3 comes from the sentence "you are arrogant." such hint is intuitively useful for deciding the relations. however, @xcite is the only prior work that tried to explicitly leverage such signal for improving dre, because such explanation annotations may not be always available @xcite . prior work can be divided into two main lines, one of which is graph-based methods. dhgat @xcite presents an attention-based heterogeneous graph network to model multiple types of features; gdpnet @xcite constructs latent multi-view graphs to model possible relationships among tokens in a long sequence, and then refines the graphs by iterative graph convo- context vector start end dialogue context trigger prediction relation prediction explicit trigger gate [cls] … … [s2] : he didn't … s3 : but… deep meaningful … [sep] tag [cls] [s2] lution and pooling techniques. another branch is bert-based (kenton and toutanova, 2019) methods @xcite . simeplere @xcite is a simple bert model with an additional refinement gate for iteratively finding high-confidence prediction. lsr @xcite is a latent structure refinement method for better reasoning in the document-level relation extraction task. although it is known that using trigger information can significantly help the performance of relation extraction, only dialogre has the annotated triggers. it is not guaranteed that utilizing the annotated triggers can generalize to other relations from other datasets, considering the discrepancy of their relation types. given the target data without trigger annotations, this paper proposes trend, a simple multitasking model with an attentional relation predictor, where it learns the general capability of finding triggers and transfers it to the unseen relations for performance improvement. the experiments show that our proposed method can effectively identify the explicit triggers and generalize to unseen relations towards great flexibility and practicality. ## proposed method the core idea of this model is to identify trigger spans and accordingly leverage such signal to improve relation extraction. we hereby propose trigger-enhanced relation-extraction network for dialogues, trend, illustrated in figure 2 . ## experiments we focus on evaluating the performance of dre on the dataset without trigger labels in order to investigate if the trigger-finding capability can be transferred across datasets/relations.