# MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting

arXiv: https://arxiv.org/abs/2407.00581 · HTML: https://mlsanity.com/paper/2407.00581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition](https://mlsanity.com/paper/1904.04446/) (2019)
- [Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations](https://mlsanity.com/paper/2105.09984/) (2021)
- [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://mlsanity.com/paper/2203.06419/) (2022)
- [Multimodal Emotion-Cause Pair Extraction in Conversations](https://mlsanity.com/paper/2110.08020/) (2021)
- [BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis](https://mlsanity.com/paper/2006.00492/) (2020)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach](https://mlsanity.com/paper/2102.09943/) (2021)
- [SentMix-3L: A Bangla-English-Hindi Code-Mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2310.18023/) (2023)
- [OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification](https://mlsanity.com/paper/2310.18387/) (2023)
- [Emotion Flip Reasoning in Multiparty Conversations](https://mlsanity.com/paper/2306.13959/) (2023)
- [MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification](https://mlsanity.com/paper/2402.01976/) (2024)
- [MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments](https://mlsanity.com/paper/2401.14681/) (2024)

## Abstract (arXiv)

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.