TW - NLP at S em E val-2024 Task10: Emotion Recognition and Emotion Reversal Inference in Multi-Party Dialogues. (2024)

Abstract: In multidimensional dialogues, emotions serve not only as crucial mediators of emotional exchanges but also carry rich information. Therefore, accurately identifying the emotions of interlocutors and understanding the triggering factors of emotional changes are paramount. This study focuses on the tasks of multilingual dialogue emotion recognition and emotion reversal reasoning based on provocateurs, aiming to enhance the accuracy and depth of emotional understanding in dialogues. To achieve this goal, we propose a novel model, MBERT-TextRCNN-PL, designed to effectively capture emotional information of interlocutors. Additionally, we introduce XGBoost-EC (Emotion Capturer) to identify emotion provocateurs, thereby delving deeper into the causal relationships behind emotional changes. By comparing with state-of-the-art models, our approach demonstrates significant improvements in recognizing dialogue emotions and provocateurs, offering new insights and methodologies for multilingual dialogue emotion understanding and emotion reversal research.

## introduction the ediref shared task at semeval 2024 encompasses three subtasks @xcite : task 1 involves emotion recognition (erc) in mixed hindi-english dialogues, task 2 focuses on emotion flipping reasoning (efr) in mixed hindi-english dialogues, and task 3 involves efr in english dialogues. in task 1 erc, the goal is to assign emotions to each utterance in the dialogue, while in task 2 and task 3 efr, the aim is to identify trigger utterances leading to emotion flipping in multi-party dialogues. the definitions of these tasks provide a crucial framework for understanding the dynamics of emotions in natural language conversations. firstly, we are committed to addressing two subtasks: emotion recognition in conversations (erc) in task 1, and emotion flipping reasoning (efr) in tasks 2 and 3. for task 1, we constructed the mbert-textrcnn-pl model based on mbert @xcite and prompt learning to identify emotions in mixed-language dialogues. by leveraging the multilingual capability of the mbert model and incorporating prompt learning, we successfully guided the model to focus on key aspects of emotion recognition. this approach can effectively handle mixed hindi and english conversations while achieving the sharing of model parameters between different languages. finally, to improve the robustness of the model, this paper integrates fgm to enhance the model's generalization ability. secondly, for tasks 2 and 3, we proposed the xgboost-ec (emotion capture) method aimed at identifying triggers of emotion flipping. we segmented dialogues into fixed-size windows and extracted emotion encodings from each window. to better encode the emotions of the final speaker, we used -1 to fill in blank emotion states within the window. then, we employed the xgboost algorithm @xcite for classification to identify windows that could potentially be triggers of emotion flipping. by guiding the model to learn patterns and features of emotion flipping triggers through annotated data during training, we were able to effectively identify triggers of emotion flipping in dialogues. in the ediref shared task at semeval 2024, our foot_0 proposed mbert-textrcnn-pl model achieved 6th place in task 1. additionally, our xgboost-ec model secured 1st place in task 2 and 5th place in task 3. ## experimental setup task 1. for the erc task 1, our model was built using the hugging face transformers library, where we directly employed pre-trained tokenizer and language models for further fine-tuning. specifically, we utilized the mbert-textrcnn-pl model with parameter configurations of a learning rate of 3e-6 and a batch size of 16 for training. we opted for the adamw optimizer to update the model parameters. to ensure the model could handle longer text sequences, we set the maximum sequence length of the tokenizer to 512. during the model's validation evaluation process, we employed the f1 score as the performance metric. hardware-wise, we utilized the nvidia rtx3090 (24g) graphics card to accelerate both model training and inference processes. task 2. for the efr task 2 and task 3, we built the xgboost-ec model based on the xgboost library. in task 2, after validation, we chose to set the scale-pos-weight parameter in the xgbclassifier to 1.08 to address the issue of imbalanced samples. additionally, we fixed the random seed to 42 to ensure reproducibility of the results. to better capture changes in emotion, we set the emotion window size to 4. task 3. in task 3, we adjusted the scale-posweight parameter in the xgbclassifier to 1.6 to accommodate different levels of sample imbalance. similarly, we fixed the random seed to 42 and set the emotion window size to 3, enabling the model to capture trends in emotion changes more effectively. ## conclusion in this paper, we focus on addressing the challenges of multilingual conversation emotion recognition and emotion flipping reasoning tasks. to this end, we propose two models to tackle these tasks. firstly, for the multilingual conversation emotion recognition task, we introduce the mbert-textrcnn-pl model. this model combines prompt learning and the mbert-textrcnn approach to better recognize emotions from multiple speakers. through prompt learning, we can provide richer contextual information, thereby accurately capturing the emotional content in the text. leveraging the features of mbert-textrcnn, this model effectively utilizes the capabilities of multilingual semantic understanding and sequence modeling to enhance the accuracy and effectiveness of emotion recognition. secondly, for task 2 and task 3 of efr, we propose the xgboost-ec (emotion capturer) model, aimed at identifying emotion instigators. this model, employing emotion windows and xgboost classifier, captures emotion instigators more effectively. the setting of emotion windows allows the model to consider the historical trend of emotion changes, leading to a more comprehensive analysis of emotional data. the application of the xgboost classifier enables efficient classification and reasoning of emotional data, thereby enhancing the model's ability to identify instigators. the introduction of these two models provides effective solutions for multilingual conversation emotion recognition and emotion flipping reason-ing tasks, offering new insights and methods for research and applications in related fields.