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- W2890465489 abstract "Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models." @default.
- W2890465489 created "2018-09-27" @default.
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- W2890465489 date "2018-01-01" @default.
- W2890465489 modified "2023-09-22" @default.
- W2890465489 title "Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates" @default.
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- W2890465489 doi "https://doi.org/10.18653/v1/d18-1069" @default.
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