HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations

Challenge:	Emotion detection in conversations is to detect the emotion for each utterance in conversations that have multiple speakers.
Approach:	They propose a transformer-based context- and speaker-sensitive model for EDC . they utilize a low-level transformer to generate local utterance representations .
Outcome:	The proposed model outperforms state-of-the-art models on three benchmark datasets.

Similar Papers

Overview

Challenge

Approach

Outcome

PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations (2024.findings-emnlp)

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Tanmay Khule, Rishabh Agrawal, Apurva Narayan

Dialogue Systems & Conversational Agents

Challenge:	Current technologies in ERC are limited due to the need for future information.
Approach:	They propose a time-series forecasting transformer that predicts pseudo-future information to overcome this constraint.
Outcome:	The proposed method achieves state of the art performance on three ERC datasets - MELD, EmoryNLP, and IEMOCap.

EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation (2024.lrec-main)

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Zhongquan Jian, Ante Wang, Jinsong Su, Junfeng Yao, Meihong Wang, Qingqiang Wu

Sentiment Analysis

Challenge:	Emotions are causally transmitted among communication participants, facilitating comprehension of intricate changes in emotional states during the conversation.
Approach:	They propose an Emotional Transition-based Emotion Recognizer that captures ET features in an emotional conversation by concatenating the most recent utterances with their corresponding speakers.
Outcome:	The proposed model is sensitive to emotions and captures ET features in the sample.

LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics (2025.coling-main)

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Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang, Lili Shan, Yulin Wu, Bingquan Liu

Sentiment Analysis

Challenge:	Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation.
Approach:	They propose a framework that uses large language models to analyze speaker characteristics . they use two-stage learning to make the models reason speaker characteristics and track emotion of the speaker .
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)

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Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He

Natural Language Interfaces

Challenge:	Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states.
Approach:	They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection.
Outcome:	The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show .

ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024.lrec-main)

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Xiujuan Xu, Xiaoxiao Shi, Zhehuan Zhao, Yu Liu

Multimodality

Challenge:	Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation.
Approach:	They propose to combine a directed acyclic graph and contextual prefixes to model historical utterances in a conversation and incorporate a contextual prefixed containing sentiment and semantics of historical .
Outcome:	The proposed model achieves state-of-the-art (SOTA) performance on several public benchmarks.

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (D19-1)

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Peixiang Zhong, Di Wang, Chunyan Miao

Natural Language Interfaces

Challenge:	Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions .
Approach:	They propose a Knowledge-Enriched Transformer where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged.
Outcome:	The proposed model outperforms state-of-the-art models on most of the tested datasets in F1 score.

Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation (2024.findings-naacl)

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Fangxu Yu, Junjie Guo, Zhen Wu, Xinyu Dai

Semantic Text Processing

Challenge:	Emotion Recognition in Conversation (ERC) is a task that aims to identify the emotions behind each utterance in a conversation.
Approach:	They propose an Emotion-Anchored Contrastive Learning framework that generates more distinguishable utterance representations for similar emotions.
Outcome:	The proposed framework achieves state-of-the-art on similar emotions and performs well on similar ones.

The MERSA Dataset and a Transformer-Based Approach for Speech Emotion Recognition (2024.acl-long)

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Enshi Zhang, Rafael Trujillo, Christian Poellabauer

Multimodality

Challenge:	Existing models for speech emotion recognition lack a comprehensive dataset to design accurate models.
Approach:	They propose to use a multimodal dataset to build a model that integrates pre-trained wav2vec 2.0 and BERT to learn hidden representations from fused representations of speech and text.
Outcome:	The proposed model predicts emotions on dimensions of arousal, valence, and dominance . it achieved competitive results on the MSP-PODCAST dataset .

HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition (2021.findings-emnlp)

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Zhiwei Yang, Jing Ma, Hechang Chen, Yunke Zhang, Yi Chang

Language Models

Challenge:	Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods.
Approach:	They propose a Hierarchical Transformer network which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner.
Outcome:	The proposed method achieves much better performance than the state-of-the-art approaches on GENIA, ACE-2004, ace-2005 and NNE datasets.

A Cross-Modality Context Fusion and Semantic Refinement Network for Emotion Recognition in Conversation (2023.acl-long)

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Xiaoheng Zhang, Yang Li

Sentiment Analysis

Challenge:	Emotion recognition in conversation studies focus on textual modality, but they lack contextual information and focus on a limited number of modalities.
Approach:	They propose a cross-modal context fusion and semantic refinement network to explore multimodal interactions and a graph-based semantic refinements transformer to solve the limitation of insufficient semantic relationship information between utterances.
Outcome:	The proposed method is compared with other state-of-the-art methods on two public benchmark datasets and shows its potential for emotion recognition.