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@article{InformSciences2024,

title = {A graph neural network with context filtering and feature correction for conversational emotion recognition},

author = {Chenquan Gan and Jiahao Zheng and Qingyi Zhu and Deepak Kumar Jain and Vitomir {v{S}}truc, },

url = {https://www.sciencedirect.com/science/article/pii/S002002552301602X?via%3Dihub

https://lmi.fe.uni-lj.si/wp-content/uploads/2023/12/InformationSciences.pdf},

doi = {https://doi.org/10.1016/j.ins.2023.120017},

year  = {2024},

date = {2024-02-01},

journal = {Information Sciences},

volume = {658},

number = {120017},

pages = {1-21},

abstract = {Conversational emotion recognition represents an important machine-learning problem with a wide variety of deployment possibilities. The key challenge in this area is how to properly capture the key conversational aspects that facilitate reliable emotion recognition, including utterance semantics, temporal order, informative contextual cues, speaker interactions as well as other relevant factors. In this paper, we present a novel Graph Neural Network approach for conversational emotion recognition at the utterance level. Our method addresses the outlined challenges and represents conversations in the form of graph structures that naturally encode temporal order, speaker dependencies, and even long-distance context. To efficiently capture the semantic content of the conversations, we leverage the zero-shot feature-extraction capabilities of pre-trained large-scale language models and then integrate two key contributions into the graph neural network to ensure competitive recognition results. The first is a novel context filter that establishes meaningful utterance dependencies for the graph construction procedure and removes low-relevance and uninformative utterances from being used as a source of contextual information for the recognition task. The second contribution is a feature-correction procedure that adjusts the information content in the generated feature representations through a gating mechanism to improve their discriminative power and reduce emotion-prediction errors. We conduct extensive experiments on four commonly used conversational datasets, i.e., IEMOCAP, MELD, Dailydialog, and EmoryNLP, to demonstrate the capabilities of the developed graph neural network with context filtering and error-correction capabilities. The results of the experiments point to highly promising performance, especially when compared to state-of-the-art competitors from the literature.},

keywords = {context filtering, conversations, dialogue, emotion recognition, graph neural network, sentiment analysis},

pubstate = {published},

tppubtype = {article}

}

@article{TextEmotionESWA,

title = {DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition},

author = {Chenquan Gan and Yucheng Yang and Qingyi Zhub and Deepak Kumar Jain and Vitomir Struc},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422016025?via%3Dihub},

doi = {https://doi.org/10.1016/j.eswa.2022.118525},

year  = {2022},

date = {2022-12-30},

urldate = {2022-08-01},

journal = {Expert Systems with Applications},

volume = {210},

abstract = {To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively.},

keywords = {attention, CNN, deep learning, dialogue, emotion recognition, fusion, fusion network, nlp, semantics, text, text processing},

pubstate = {published},

tppubtype = {article}

}