Structured Representation Learning for Online Debate Stance Prediction.

Chang Li     Aldo Porco     Dan Goldwasser    
International Conference on Computational Linguistics (COLING), 2018


Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.

Bib Entry

    author = "Chang Li and Aldo Porco and Dan Goldwasser",
    title = "Structured Representation Learning for Online Debate Stance Prediction.",
    booktitle = "International Conference on Computational Linguistics (COLING)",
    year = "2018"