End-to-End Emotion Semantic Parsing (2024)

Abstract: Emotion detection is the task of automatically associating one or more emotions with a text. The emotions are experienced, targeted, and caused by different semantic constituents. Therefore, it is necessary to incorporate these semantic constituents into the process of emotion detection. In this study, we propose a new task called emotion semantic parsing which aims to parse the emotion and semantic constituents into an abstract semantic tree structure. In particular, we design an end-to-end generation model to capture the relations between emotion and all the semantic constituents, and to generate them jointly. Furthermore, we employ a task decomposition strategy to capture the semantic relation among these constituents in a more cognitive and structural way. Experimental results demonstrate the importance of the proposed task, and indicate the proposed model gives superior performance compared to other models.

## introduction the goal of emotion detection is to automatically detect or categorize the emotional states of human according to some inputs. nowadays, emotion detection can be found in a broad range of applications, including but not limited to emotional support @xcite , human-computer interaction @xcite and healthcare surveillance @xcite . henceforth, emotion detection has attracted increasing attention from both research community and industry in recent years @xcite . in literature, most studies focus on detecting emotion from the given text. for example, featurebased works @xcite and deep learning approaches, including rnns @xcite he clenched his fists as he watched the bully taunt his little brother. 2019), transformers @xcite , and pretrained language models @xcite are used in emotion detection. nevertheless, over the past few years, numerous studies @xcite @xcite have indicated that the emotions may be experienced, targeted, and/or caused by different semantic constituents, and can be linked to form abstract semantic structures. however, most existing works only take "cause" into consideration, neglecting the intricate relationships between emotion constituents, such as the strong relationship between trigger and emotion, as well as the similarity of sentiment and emotion labels. this limitation makes acquiring a nuanced understanding of the interplay between emotion and semantic constituents challenging, thereby hindering the model's capability to thoroughly comprehend emotional expression. by addressing these complexities, we believe it can serve as a foundational component for improving results in downstream real-world tasks by providing a more comprehensive understanding of emotional dynamics and their influence. to tackle the above problem, we propose a new task called emotion semantic parsing which aims to parse the emotion and semantic constituents into an abstract semantic tree structure. in this work, we consider five emotion-related semantic constituents into this task, including emotion, sentiment, trigger, holder, and target. as shown in figure 1 , emotion and sentiment refer to the specific emotion and sentiment labels of the sentence; trigger refers to a trigger word or expression that describes an emotion; and the constituents of holder and target correspond to the person or entities towards whom or from whom the emotion is directed, respectively. moreover, we use the form of the abstract semantic tree structure to model the relationship between these constituents, we use the trigger node to represent the whole abstract semantic tree, and we use the virtual feelings and entities nodes to compass other constituent nodes. the tree structure can be considered as a semantic representation in order to better represent the structure of the emotionrelated constituents. as shown in figure 1 , the tree structure models a sentence using a rooted directed acyclic graph, highlighting its main elements (e.g. emotion, triggers) and semantic relations. it can thus potentially reveal a more comprehensive and complete semantic structure for extracting these constituents. furthermore, since the emotion and semantic constituents are correlated in the form of the abstract semantic tree structure, we design an end-to-end generative model to predict them jointly. specifically, our proposed model begins by decomposing the original task into several sub-tasks, as shown in figure 2 . subsequently, the proposed model utilizes template-based prompt learning to extract each semantic constituent and semantic constituents pair separately for each sub-tasks. finally, the proposed model leverages the acquired knowledge from sub-tasks to generate all semantic constituents in the main task. we conducted extensive experiments on the unified benchmark. experimental results demonstrate the importance of the proposed task, and indicate the proposed model gives superior performance compared to other models. further analysis shows the effect of the proposed abstract semantic tree structure and decomposition strategies. ## related works in the domain of emotion analysis, previous studies have mainly focused on text-based emotion detection. early feature-based work often relied on traditional machine learning algorithms and manually crafted word-level emotion lexicons @xcite . as deep neural networks progressed, emotion detection methods began utilizing various networks, such as rnns @xcite and transformers @xcite , to learn the emotion-related representation for detection. recently, pre-trained language models @xcite have dominated this area with its flexibility and rich internal knowledge @xcite . the emotions are not expressed individually, and can be experienced, targeted, and caused by different semantic constituents @xcite @xcite . most recent studies focus on emotion cause extraction task @xcite @xcite , with the goal of extracting the potential pairs of emotions and corresponding causes in the document and conversation scenario. many end-to-end approaches have been proposed. these works can be broadly categorized into two main groups: some focus on multi-task learning @xcite , while others transform the original task into a unified sequence labeling problem @xcite . recently, @xcite developed a rule-based system to extract emotion causes on social media from constituency parsing trees. @xcite proposed emotion cause triplet extraction to extract emotion-cause-category triplets for fine-grained emotion cause analysis. different from prior studies, we introduce a novel task, termed emotion semantic parsing. this task seeks to comprehensively examine the impact of various semantic constituents on emotional expressions. to achieve this, we develop an endto-end generative model specifically designed to capture the intricate relationships between emotion and a wide range of semantic constituents. ## preliminaries as shown in figure 2 , we introduce a novel task termed emotion semantic parsing. this task focuses on the generation of an abstract semantic tree structure that encapsulates both emotion and semantic constituents. to achieve this, we develop an end-to-end generative model specifically tailored for the generation of the abstract semantic tree structure. our proposed model adopts a decomposition-based strategy, breaking down the complex task into several more manageable subtasks. specifically, our proposed model begins by decomposing the original task into several subtasks. finally, the proposed model leverages the acquired knowledge from sub-tasks to generate all semantic constituents in the main task. in this section, we introduce two important issues of this task: abstract semantic tree construction and the abstract semantic tree parsing model. the decomposition-based strategy for abstract semantic tree parsing will be discussed in the next section. ## decomposition-based semantic tree parsing model in this study, we propose a decomposed-based generative model to tackle the above challenges. the motivation is to alleviate the complexity of the original task by decomposing it into several sub-tasks. as shown in figure 2 , our proposed model can be extract the trigger + text (trigger) (a) mono parsing (taking trigger generation as an example) extract both trigger and holder + text (trigger) (b) trigger related pair parsing (taking trigger and holder generation as an example) (entities) (holder) extract both holder and target + text (entities) (c) entities pair parsing (holder) (target) extract both emotion and sentiment + text (feelings) (d) feelings pair parsing (emotion) (sentiment) figure 3: examples of task decomposition strategies. due to the space limit, prompt texts in this figure are shortened. original prompt texts are presented in section 4.1. ## experimentation in this section, we introduce the datasets used for evaluation and the baseline methods employed for comparison. we then report the experimental results conducted from different perspectives, and analyze the effectiveness of the proposed model with different factors.