POP - CEE : Position-oriented Prompt-tuning Model for Causal Emotion Entailment (2024)

Abstract: The objective of the Causal Emotion Entailment (CEE) task is to identify the causes of the target emotional utterances in a given conversation. Most existing studies have focused on a fine-tuning paradigm based on a pretrained model, e.g., the BERT model. However, there are gaps between the pretrained task and the CEE task. Although a pretrained model enhances contextual comprehension to some extent, it cannot acquire specific knowledge that is relevant to the CEE task. In addition, in a typical CEE task, there are peculiarities in the distribution of the positions with different emotion types of emotion utterances and cause utterances in conversations. Existing methods employ a fixed-size window to capture the relationship between neighboring conversations; however, these methods ignore the specific semantic associations between emotions and cause utterances. To address these issues, we propose the Position-oriented Prompt-tuning (POP-CEE) model to solve the CEE task in an end-to-end manner. Specifically, we can model the CEE task by designing prompts with multiple unified goals and by exploring the positional relationship between emotion and cause utterances using a position constraint module. Experimental results demonstrate that the proposed POP-CEE model achieves state-of-the-art performance on a benchmark dataset. Ourcode and data can be found at: https://github.com/Zh0uzh/POP-CEE.

## introduction social media's recent expansion has increased interest in emotion analysis in conversations (eac) in the natural language processing (nlp) field. existing eac efforts primarily focus on emotion recognition in conversation (erc), which identifies the types of emotions conveyed in utterances. however, @xcite has argued that recognizing a speaker's emotion is insufficient. in other words, further reasoning is required, e.g., mining the causes behind the speaker's emotion. thus, they proposed the reccon task, which comprises two sub-tasks, i.e., causal span extraction (cse) at the word or phrase level and causal emotion entailment (cee) at the utterance level. in this paper, we focus on the cee task, which attempts to predict which specific utterances in a given dialogue history contain the causes of non-neutral emotions in the target utterance. for the cee task, @xcite proposed a baseline approach, treating the task as a natural language inference problem. however, this method only performs simple inference and does not consider the characteristics of the dialogue text. thus, @xcite , @xcite , and @xcite characterized the structure of the conversation and the information of the emotion and speaker in the conversation text by designing different network models, and @xcite , @xcite incorporated the commonsense knowledge into the inference using graph models. these previous studies considered different dialogue characteristics. however, they ignored the relationship between emotion types and the positions of the corresponding cause clauses. when a speaker displays a particular emotion, the different types of emotion can reveal the position of the cause. for example, we consider the conversation scenarios shown in figure 1 . here, for the happiness emotion, when speaker a's utterance contains the happiness emotion, speaker b's subsequent utterance is more likely to contain happiness because positive emotions are contagious among speakers. in other words, when a speaker communicates positive emotions, those around them are influenced and experience similar positive emotions. we observe that the underlying causes of positive emotions are typically the same, and the primary cause is the initial utterance of speaker a expressing happiness. in terms of the surprise emotion, when speaker b's utterance contains the surprise emotion, we know that speaker b must not have known about it beforehand. the surprise emotion typically only lasts for a short period. therefore, the cause of surprise is generally contained in the utterance nearest to the emotion utterance. thus, surprise is typically caused by the speaker's words most intimate to the surprise emotion, frequently speaker a. for negative emotions, e.g., disgust and fear, in a conversational situation, the goal of the conversation is to transform the negative emotion into a neutral or positive emotion. when speaker a expresses a negative emotion, speaker b will typically persuade speaker a with a neutral feeling. the conversation tends to move in the direction of one person complaining and the other comforting, and the cause of the negative emotion is generally in the first utterance in which speaker a expresses the negative emotion. in rare cases, e.g., when both speakers are arguing, speakers a and b may represent the same emotion. the position between the emotion and the cause can facilitate effective utterance extraction in conversations. these linguistic phenomena are based on our statistics on the reccon-dd dataset, and the relationship between emotion and position is shown in thus, we propose the position-oriented prompttuning model for cee (pop-cee). multiple templates are proposed to guide the pretrained model from a semantic perspective combined with constraints to filter out the model's obviously incorrect prediction results. based on the observed positional relationship between the emotions and the causes in the target conversations, we construct multiple templates modelling the cee tasks to direct the model's attention to the position between the emotions and causes. then, the positional constraints module filters the obtained predictions. we conducted extensive experiments on benchmarks, and the results demonstrate that the proposed framework outperforms all baselines on the experimental dataset. in addition, we conducted several studies and analyses to explore each module's effectiveness on the cee task. the primary contributions of this study are summarized as follows. early work in sentiment analysis was applied to opinion mining tasks @xcite to recognize and extract opinions from text data. more recently, the work in sentiment analysis has been applied to a wider range of tasks, e.g., allowing for the structure of the model is shown at the top, and the detailed structure of the emotion prompt module is shown below. first, we input the target utterance, the candidate utterance, and their speakers for reasoning. then, the emotion prompt module selects the corresponding prompt according to the emotion type of the target utterance. then, the average value of the reasoning result and the reasoning result of the basic-prompt are taken to obtain the probability that the candidate utterance contains the cause of the emotion. the position constraint module then further constrains the probability according to the positional relationship between the target utterance and the candidate utterance to obtain the final prediction result. candidate utterances containing the target utterance's cause are marked with pentagrams. initially, the extraction of target emotional causes was primarily realized using rule-based methods @xcite . @xcite constructed a public ece dataset using city news as a corpus and solved the emotional causes extraction problem as a question answering task @xcite . then, in conjunction with the ece task, @xcite proposed the emotional causes pair extraction (ecpe) task and used a two-part framework for it to process. many end-to-end improvement methods emerged subsequently @xcite @xcite @xcite @xcite @xcite . however, the ece and ecpe datasets use news articles as the corpus, and the complexity of reasoning is lower because most expressions in news articles are more formal, the expressed emotions are typically very clear, and the information density is higher. to further reason about emotions, @xcite proposed the reccon task, which is based on two datasets, i.e., the iemocap @xcite and dailydialogue @xcite datasets. the reccon task comprises two different subtasks, i.e., the cse and cee tasks, and most current work has focused on solving cee, whose goal is to determine whether the target utterance in a given conversation history contains the cause of the target emotion utterance. to address this task, @xcite proposed tsam to capture intra-speaker and inter-speaker emotional influences in the global view, @xcite proposed dam to capture the interactions between utterances and session-specific structures, @xcite proposed kbcin to reason about emotional causes combined with commonsense knowledge, and @xcite constructed a joint framework using a window transformer to capture the short-time effects of information transfer between multiple utterances. in this paper, we solve the cee task based on prompt learning. ## conclusion in this study, we have applied prompt learning to the cee task and designed a new prompt learning-based paradigm to strengthen the pretrained model's knowledge of the positional relationship between an emotion and its cause utterances to guide the model to recognize the corresponding emotional cause utterance effectively. experimental results demonstrate that the proposed model outperforms several state-of-the-art models, and additional ablation experiments and analyses verify the effectiveness of each component implemented in the proposed model.