應用階層可解構式注意力模型於新聞立場辨識任務

The goal of News Stance Detection task is to detect whether the stance of a news article is neutral, approval or opposition with respect to a given query. The task is similar to Natural Language Inference (NLI) task, which aims to determine if one given statement (a premise) semantically entails another given statement (a hypothesis). Since most news articles hold neutral stances with respect to the given query, the training data is often unbalanced. In this paper, we proposed a Hierarchical Model based on the Decomposable Attention Model for NLI tasks to compare individual sentences with the given query and jointly predict the stance of the complete article. For the data imbalance problem, we heuristically create opposite queries and label supporting news articles from unrelated ones of the original query to identify unrelated news articles. The experiment result showed that the performance of our architecture is better than other models.