# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 1 Introduction  
Relation extraction (RE) aims to extract structured knowledge from unstructured text and is widely used in various downstream tasks such as knowledge base construction and question answering [1]. Although most existing RE systems focus on sentence-level RE and have achieved promising results on several benchmark datasets [2, 3], they are limited in their representation ability to extract relational facts from multiple sentences [4]. The capability only to capture intra-sentence relational facts cannot cover numerous relational facts that appear across multiple sentences in a document or with more than one speaker in a dialogue, and understanding inter-sentence and intra-document information is more significant in practical scenarios [5, 6]. Therefore, several studies have shifted their focus toward more challenging but practical RE tasks that require extracting relational information from more extended and complicated contexts, such as documents and dialogues [7, 8]. The dialogue-based relation extraction (DialogRE) task aims to predict the relation(s) between two entities that appear in an entire dialogue and requires the cross-sentence RE technique in the conversational setting with multi-speakers and multi-utterances [9]. Due to the multi-occurrences of speakers and utterances in a dialogue, meaningful informationthat supports the relational facts is spread over the entire dialogue, resulting in low relational information density. To effectively capture and understand the scattered relational information in a dialogue, it is essential to directly exploit the pre-trained language model (PLM) knowledge by appropriately guiding the model on which information is significant in the conversation [10]. Therefore, to leverage the knowledge inherent in PLM and guide it to identify important information in conversations, we adopt a prompt-based fine-tuning approach along with a trigger generation method in the DialogRE task. Concerning the direct exploitation of the knowledge of PLM first, a prompt-based learning approach has been proposed and is advantageous in consistency in learning. Unlike the conventional fine-tuning approach, which utilizes the representation of a special classification token [CLS] from an additional classifier, the prompt-based learning approach directly exploits the learned knowledge of a pre-trained language model by alleviating discrepancy [11]. In particular, a prompt-based approach using PLMs such as Bidirectional Encoder Representations from Transformers (BERT) [12] solves downstream tasks by regarding them as a cloze task using the [MASK] token as a direct predictor, resulting in bridging the gap of learning objectives between pre-training phase and downstream task. Moreover, providing appropriate guidance on which contextual representation is remarkable for the model in the DialogRE system helps to alleviate the challenge of dialogue relation extraction due to the low relational information density. The trigger, which can be described as a potential explanatory element, is defined as \"the smallest span of continuous text that clearly indicates the existence of a given relation\" and plays an essential role in understanding contextual features in the dialogue [9]. Table 1 shows a dialogue example that contains multiple entity pairs and triggers. For example, the first relation (R1) can be easily predicted when the word \"mom\" is accurately captured and predicted by the model, but there are no triggers aidful for the prediction in the cases of the relation types R3 and R4. As the amount of annotated triggers in the dataset is limited, this scarcity of triggers leads to the difficulty in providing guidance on which information is significant to the model for predicting relations between a given entity pair. To these ends, we explore a prompt-based learner with trigger generation for dialogue relation extraction to take advantage of the inherent knowledge in PLMs and guide them to identify crucial information in dialogues.