Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names (2023)

Abstract: Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalability and support diverse, unseen relation extraction, this paper proposes a method for leveraging the ability to capture triggers and relate them to previously unseen relation names. Specifically, we introduce a model that enables zero-shot dialogue relation extraction by utilizing trigger-capturing capabilities. Our experiments on a benchmark DialogRE dataset demonstrate that the proposed model achieves significant improvements for both seen and unseen relations. Notably, this is the first attempt at zero-shot dialogue relation extraction using trigger-capturing capabilities, and our results suggest that this approach is effective for inferring previously unseen relation types. Overall, our findings highlight the potential for this method to enhance the scalability and practicality of DRE systems.

## introduction relation extraction (re) is a key natural language processing (nlp) task that identifies the semantic relationships between arguments in various types of text data. it involves extracting relevant information and representing it in a structured form for downstream applications @xcite @xcite . dialogue relation extraction (dre) is a specialized area of re that focuses on identifying semantic relationships between arguments in conversations. recent dre research has used diverse methods to improve relation extraction performance, including constructing dialogue graphs @xcite , identifying explicit triggers @xcite , and using prompt-based fine-tuning approaches @xcite . 1 code: https://github.com/miulab/unseendre . supervised training for re tasks can be timeconsuming and expensive due to the requirement for a large amount of labeled data. models trained on limited data can only predict the relations they have been trained on, making it challenging to identify similar but unseen relations. hence, recent research has explored methods that require only a few labeled examples or no labeled examples at all, such as prompt-based fine-tuning @xcite . additionally, @xcite improved zero-shot performance by transforming the re task into an entailment task. however, this approach has not yet been applied to dre due to the challenge of converting long conversations into nli format. in this work, we observe that different relations may be dependent on each other, such as the parentchild relationship listed in to achieve this, we need to identify the key information of the relation as a tool for relation reasoning during inference. we adopt the approach proposed in @xcite , which achieves remarkable results in dre by capturing explainable keywords in a dialogue for guiding relation extraction. by leveraging such trigger-capturing capabilities, our proposed model can better deduce unseen relations from known relations and associated triggers. therefore, the proposed dre model is more practical, as it can generalize to unseen relations. ## proposed approach prior work on classical dre has treated it as a multi-class classification problem, which makes it challenging to scale to unseen relation scenarios. to enable a zero-shot setting, we reformulate the multi-class classification task into multiple binary classification tasks by adding each relation name as input, as illustrated in figure 1 . the binary classification task predicts whether the subject and object in the dialogue belong to the given relation. this approach is equivalent to predicting whether a set of subject-object relations is established, which can estimate any relations based only on their names (or natural language descriptions). ## experiments we conducted experiments using the dialogre dataset, which is widely used as a benchmark in the field. to assess our model's zero-shot capability, we divided the total of 36 relations into 20 seen and 16 unseen types detailed in the appendix. we only train our model on data related to seen relation types. during training, we set the learning rate to 3e-5 and used a geforce rtx 2080 ti. the training process involves 10 epochs without early stopping foot_0 , and the number of negative samples was 3. to ensure a fair comparison with prior work @xcite , we use the same testing set for evaluation. ## conclusion this paper introduces a novel approach for zero-shot dialogue relation extraction by relating explainable trigger words and relation names. our proposed method effectively utilizes trigger-capturing capability and demonstrates a significant improvement in inferring unseen relations. the experimental results on benchmark data show that our approach achieves better generalization and practicality, making it a promising solution for real-world applications.