MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting
- URL: http://arxiv.org/abs/2407.00581v1
- Date: Sun, 30 Jun 2024 03:59:04 GMT
- Title: MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting
- Authors: Al Nahian Bin Emran, Amrita Ganguly, Sadiya Sayara Chowdhury Puspo, Nishat Raihan, Dhiman Goswami,
- Abstract summary: We present MasonTigers' participation in SemEval-2024 Task 10, a shared task aimed at identifying emotions in code-mixed dialogues.
Our team, MasonTigers, contributed to each subtask, focusing on developing methods for accurate emotion recognition and reasoning.
We attained impressive F1-scores of 0.78 for the first task and 0.79 for both the second and third tasks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, we present MasonTigers' participation in SemEval-2024 Task 10, a shared task aimed at identifying emotions and understanding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for Hindi-English code-mixed dialogues, emotion flip reasoning for Hindi-English code-mixed dialogues, and emotion flip reasoning for English dialogues. Our team, MasonTigers, contributed to each subtask, focusing on developing methods for accurate emotion recognition and reasoning. By leveraging our approaches, we attained impressive F1-scores of 0.78 for the first task and 0.79 for both the second and third tasks. This performance not only underscores the effectiveness of our methods across different aspects of the task but also secured us the top rank in the first and third subtasks, and the 2nd rank in the second subtask. Through extensive experimentation and analysis, we provide insights into our system's performance and contributions to each subtask.
Related papers
- Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN)
EDEN recognizes emotion and causes in an explicitly thinking way.
It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
arXiv Detail & Related papers (2024-06-07T08:58:29Z) - SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations [53.60993109543582]
SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, aims at extracting all pairs of emotions and their corresponding causes from conversations.
Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE)
In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
arXiv Detail & Related papers (2024-05-19T09:59:00Z) - IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings [4.679320772294786]
We propose a transformer-based speaker-centric model for the Emotion Flip Reasoning task.
For sub-task 3, the proposed approach achieves a 5.9 (F1 score) improvement over the task baseline.
arXiv Detail & Related papers (2024-04-06T06:47:44Z) - Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system.
We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context.
Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
arXiv Detail & Related papers (2024-04-03T08:48:50Z) - LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Task [3.489826905722736]
SemEval 2024 introduces the task of Multimodal Emotion Cause Analysis in Conversations.
This paper proposes models that tackle this task as an utterance labeling and a sequence labeling problem.
In the official leaderboard for the task, our architecture was ranked 8th, achieving an F1-score of 0.1759 on the leaderboard.
arXiv Detail & Related papers (2024-04-02T16:32:49Z) - SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in
Conversation (EDiReF) [61.49972925493912]
SemEval-2024 Task 10 is a shared task centred on identifying emotions in code-mixed dialogues.
This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip reasoning for English dialogues.
A total of 84 participants engaged in this task, with the most adept systems attaining F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks.
arXiv Detail & Related papers (2024-02-29T08:20:06Z) - Explaining (Sarcastic) Utterances to Enhance Affect Understanding in
Multimodal Dialogues [40.80696210030204]
We propose MOSES, a deep neural network, which takes a multimodal (sarcastic) dialogue instance as an input and generates a natural language sentence as its explanation.
We leverage the generated explanation for various natural language understanding tasks in a conversational dialogue setup, such as sarcasm detection, humour identification, and emotion recognition.
Our evaluation shows that MOSES outperforms the state-of-the-art system for SED by an average of 2% on different evaluation metrics.
arXiv Detail & Related papers (2022-11-20T18:05:43Z) - M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database [139.08528216461502]
We propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED.
M3ED contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances.
To the best of our knowledge, M3ED is the first multimodal emotional dialogue dataset in Chinese.
arXiv Detail & Related papers (2022-05-09T06:52:51Z) - COSMIC: COmmonSense knowledge for eMotion Identification in
Conversations [95.71018134363976]
We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations.
We show that COSMIC achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets.
arXiv Detail & Related papers (2020-10-06T15:09:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.