UIC NLP GRADS at S em E val-2024 Task 3: Two-Step Disjoint Modeling for Emotion-Cause Pair Extraction (2024)

Abstract: Disentangling underlying factors contributing to the expression of emotion in multimodal data is challenging but may accelerate progress toward many real-world applications. In this paper we describe our approach for solving SemEval-2024 Task #3, Sub-Task #1, focused on identifying utterance-level emotions and their causes using the text available from the multimodal F.R.I.E.N.D.S. television series dataset. We propose to disjointly model emotion detection and causal span detection, borrowing a paradigm popular in question answering (QA) to train our model. Through our experiments we find that (a) contextual utterances before and after the target utterance play a crucial role in emotion classification; and (b) once the emotion is established, detecting the causal spans resulting in that emotion using our QA-based technique yields promising results.

## introduction the task of emotion cause analysis in conversations @xcite aims to decipher the expression of human emotion in conversational data, either through unimodal (text-only) or multimodal (e.g., with the addition of video and/or audio) information. on a fundamental level, this is a complex two-part problem: emotion must be identified for a given utterance, and the span of dialogue causing that emotion must subsequently be recognized. 1 semeval-2024 task #3 @xcite was organized around solving this problem, broken into two subtasks varying in the data allowed to build the solution; in sub-task #1, identification of emotion cause was limited to the use of only text information. we address sub-task #1 in this paper. we pursued two strategies in our approach toward solving the task. first, we trained a question answering (qa) model @xcite to extract causal spans given the reference emotions for non-neutral utterances within the training set. in doing so, we achieved comparable results to those reported by @xcite and @xcite , the latter of which is a popular benchmark for this task. next, we devised a two-step disjoint model that separately learns to classify emotion and extract causal spans during training. during inference we (1) run the emotion classifier, enriching the test set with emotion labels; and (2) run inference on the qa model to extract the causal spans. our approach achieved third place according to the primary task metric (a weighted-average proportional f 1 ) and 2 nd place on the secondary metric (weighted-average strict f 1 ; see §a.2 for results on all relevant task metrics). we elaborate on our findings in the remainder of this paper. foot_0 2 background ## system overview we model the task to maximize the probability of finding emotion-cause pairs, (e i , c i ), for the given conversation context x. we disjointly train models with parameters θ and ϕ to estimate the emotion e i from x and the causal span c i from (e i , x), respectively. we approximate x to the prompts x e and x c to provide the appropriate contextual information to our models, as shown in equation 1 . our approach is a two-step process, by which we first identify e i for the given utterance u i in a conversation from x e , and then identify the causal span c i for u i in all cases when e i ̸ = neutral. we fine-tune separate pre-trained language models (plms) for these two steps. while fine-tuning for emotion cause spans, we use the emotion labels provided as part of the training set to construct x c . emotion classifier. we use a roberta base model @xcite as the backbone of our emotion classification model, with a weighted cross entropy loss to penalize emotion label predictions. we use class weights from our training set as weighing terms for the loss function, and fine-tune for 20 epochs using the adamw optimizer @xcite with linear rate scheduler with 500 warm-up steps. we use the learning rate 5 × 10 -5 with 0.01 weight decay rate, and select the best epoch based on weighted f 1 . the input prompt to this model is a space-separated concatenation of u i , the separator token proposed by @xcite , and all utterances in the conversation (u @xmath0, ..., u n }) space-separated in sequential order, as shown in figure 2 . emotion cause classifier. we frame emotion cause classification as a qa task. to avoid asking our model to answer impossible questions, we skip utterances where the predicted label is neutral. we use a spanbert base model @xcite fine-tuned on squad2.0 @xcite . 3 we then further fine-tune this model on our task. the input prompt to this model is: what caused the e i in the current utterance? <@xmath1. for fine-tuning span-bert, we change the batch size from 32 to 12 and the maximum sequence length from 512 to 400. we set the learning rate to 2 × 10 -5 and train the model for five epochs. figure 4 shows inference x c the current utterance is -u i .what caused the e i in the current utterance? [sep] sp 1 : u 1 sp 2 : u 2 ...sp n : u n spanbert (finetuned on squad 2.0) end logits start logits cross entropy loss figure 3: training the emotion causal classifier. input x: [(u 1, sp 1 ),(u 2, sp 2 ), ... (u n, sp n )] x c the current utterance is -u i . what caused the e i in the current utterance?[sep] sp 1 : u 1 sp 2 : u 2 ...sp n : u n inference on spanbert x e u @xmath2... u n inference on roberta output e: [e 1 ,e 2 ,... e n ] output c, e: [(u i , e i , u j ), ...] using our proposed system. we first perform inference on our emotion classifier for the test dataset to augment test x c with emotion labels e i , and then perform inference on our emotion cause classifier. ## experimental setup ecf is already split into train and test sets. we separate a dev set from train by holding out the last 20% of the training data. we make use of pre-trained roberta @xcite and span-bert @xcite ) models from hugging-face. other details regarding our hardware and software libraries can be found in §a.3. for training the emotion classifier we make use of weighted f 1 score, choosing the best performing model based on this metric. while training the emotion cause classifier, we select models based on metrics defined by @xcite for span-based learning with plms: unweighted exact match, and f 1 score. ## conclusion we introduce a disjoint model comprising an emotion classifier and an emotion-cause classifier. our system addresses emotion cause extraction competitively based on the official leaderboard and on follow-up analyses ( §5). we set out with the objectives of developing a disjoint model making use of the entire conversation to identify emotions in utterances and using a well-known qa paradigm to extract the causes of the emotions, and we achieve this with varying degrees of success. we observe that emotion classification is harder than emotion cause extraction when emotion annotations are present (tables 4 and 5 ), and that when the model assigns emotions correctly, it also has a greater chance of extracting causal spans correctly (