HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition

Wenxiang Jiao, Haiqin Yang, Irwin King, Michael R. Lyu

Abstract
In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, Hi-GRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed Hi-GRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.
Anthology ID:
N19-1037
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
397–406
Language:
URL:
https://aclanthology.org/N19-1037
DOI:
10.18653/v1/N19-1037
Bibkey:
Cite (ACL):
Wenxiang Jiao, Haiqin Yang, Irwin King, and Michael R. Lyu. 2019. HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 397–406, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition (Jiao et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/starsem-semeval-split/N19-1037.pdf
Code
 wxjiao/HiGRUs
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