2022 |
Gan, Chenquan; Yang, Yucheng; Zhub, Qingyi; Jain, Deepak Kumar; Struc, Vitomir DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition Članek v strokovni reviji V: Expert Systems with Applications, vol. 210, 2022. Povzetek | Povezava | BibTeX | Oznake: attention, CNN, deep learning, dialogue, emotion recognition, fusion, fusion network, nlp, semantics, text, text processing @article{TextEmotionESWA, 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. |
Objave
2022 |
DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition Članek v strokovni reviji V: Expert Systems with Applications, vol. 210, 2022. |