Di Chen


Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates
Di Chen | Jiachen Du | Lidong Bing | Ruifeng Xu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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.


SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity
Carmen Banea | Di Chen | Rada Mihalcea | Claire Cardie | Janyce Wiebe
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)