DHF-NetGan, Chenquan (Avtor) Yang, Yucheng (Avtor) Zhu, Qingyi (Avtor) Jain, Deepak Kumar (Avtor) Štruc, Vitomir (Avtor) emotion recognitiontext processingNLPdeep learningdialogue emotion recognitioncontextual informationfine-grained informationhierarchical featureinteractive fusionTo 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.20222022-09-06 08:15:57Članek v reviji139610sl