A Hierarchical Decomposable Attention Model for News Stance Detection

摘要

In News Stance Detection task, we have to judge whether the stance of news is neutral, partial approval or opposition to a given query. This task is similar to the Natural Language Inference (NLI) task which accepts two sentences as input and determine whether the given two sentences are irrelevant, entailment or contradiction. A news article is a discourse composed of multiple sentences, so it may also help to determine the news stance by analyzing the relationship between the sentences in the article (called Discourse Analysis).In the related work for Discourse Analysis, most models are trained with the relationship between “sentences” (Discourse Relation) in the articles; News Stance Detection is to find relation between “query” and “article”, so the discourse relation in the articles, and the relationship between each sentence in the article and the query is unknown. At the same time, in our training data, few of news articles hold different stance toward the queries, such a data imbalance problem makes the training of models more difficult, which is also a challenge of this task. In this paper, we proposed a Hierarchical Decomposable Attention Model to solve News Stance Detection task which refer to the Decomposable Attention Model (Parikh et al. 2016) for NLI tasks and the hierarchical way to deal with discourse (Durmus et al. 2019). The experiment result showed that the performance of our architecture is better than other models. For the data imbalance problem, we added data labeled with unrelated and proved this way can improve the ability of the model to identify unrelated data.