Abstract
Conversational emotion recognition has gained significant attention in data mining and text mining recently. Most existing methods only consider the utterance in conversations as a temporal sequence and ignore the fine-grained emotional clues in the compositional structure, where the non-ignorable semantic transitions and tone enhancement are implied. Consequently, such models hardly capture accurate semantic features of the utterance, which results in the accumulation of incorrect emotional features in the memory bank. To address this problem, we propose a novel framework, Tree-based Attention Networks with Transformer Pre-training (TANTP), which incorporates contextual representations and recursive constituency tree structure into the model architecture. Different from merely modeling the utterance in light of the time order, TANTP could effectively capture compositional emotion semantics of utterance features for the memory bank, where complex semantic transitions and emotional progression are difficult to be revealed by previous conventional sequential methods. Experimental results conducted on two public benchmark datasets demonstrate that TANTP could achieve superior performance compared with other state-of-the-art models.
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Liu, H., Lin, H., Chen, G. (2021). TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_58
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DOI: https://doi.org/10.1007/978-3-030-75765-6_58
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