Abstract
Stance detection aims at identifying the stance (favor, against or neutral) of a text towards a specific target of opinion. Recently, there is a growing interest in using neural models for stance detection, but there are still some challenges to be solved. Firstly, it is difficult to associate text with target because targets are not always discussed explicitly in texts. However, existing methods always roughly model the representations of text and target on task-specific and limited corpus without considering the indispensable external information. Secondly, different from categories in normal classification task, we find that stances in stance detection task are not independent to each other. We study this observation and find it would be more effective to learn each stance individually. But all previous approaches ignore the correlation. To address these two challenges effectively, we introduce a Stance-wise Convolution Network (SCN) including two novel modules. Specifically, we first use a Text-Target Encoder module to subtly incorporate the pre-trained BERT into our model to learn more reasonable text-target representations. Then we propose a Stance-wise Convolution module to better learn stances by absorbing the correlation between stances. We evaluate our method on real-world dataset and the experimental results show that our proposed method achieves the state-of-the-art performance.
D. Yang and Q. Wu—Contribute equally.
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This work is supported by State Grid Technical Project (No. 52110418002W).
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Yang, D. et al. (2020). Stance Detection with Stance-Wise Convolution Network. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_44
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