Abstract
Extracting emotion cause and experiencer from text can help people better understand users’ behavior patterns behind expressed emotions. Machine reading comprehension framework explicitly introduces a task-oriented query to boost the extraction task. In practice, how to learn a good task-oriented representation, accurately locate the boundary, and extract multiple causes and experiencers are the key technical challenges. To solve the above problems, this paper proposes BERT-based Machine Reading Comprehension Extraction Model with Multi-Task Learning (BERT-MRC-MTL). It first introduces query as prior knowledge and obtains text representation via BERT. Then, boundary-based and tag-based strategies are designed to select characters to be extracted, so as to extract multiple causes or experiencers simultaneously. Finally, hierarchical multi-task learning structure with residual connection is adopted to combine the answer extraction strategies. We conduct experiments on two public Chinese emotion datasets, and the results demonstrate the efficacy of our proposed model.
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Notes
- 1.
HLTEmotionml is available at http://119.23.18.63/?page_id=694
- 2.
CEAC is available at https://github.com/liupengyuan/EmotionAction_EmotionInference
- 3.
The Chinese character embedding is available at https://github.com/Embedding/Chinese-Word-Vectors
- 4.
BERT-Base-Chinese is available at https://huggingface.co/bert-basechinese/tree/main
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Acknowledgement
This work was supported in part by the National Key Research and Development Program of China under Grant 2020AAA0103405, the National Natural Science Foundation of China under Grants 62071467,71621002 and 71902179, as well as the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA27030100.
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Qian, H., Li, Q., Tang, Z. (2021). A Multi-Task MRC Framework for Chinese Emotion Cause and Experiencer Extraction. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_9
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