Stance detection is the task of classifying the attitude expressed in a text towards a target such as "Climate Change is a Real Concern" to be "positive", "negative" or "neutral". Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms the independent encoding of tweet and target. Performance improves even further when the conditional model is augmented with bidirectional encoding. The method is evaluated on the SemEval 2016 Task 6 Twitter Stance Detection corpus and achieves performance second best only to a system trained on semi-automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state-of-the-art results.

文章探讨基于目标的立场检测，使用条件LSTM编码建立tweet的表示并考虑了目标未提及且缺乏训练数据的情况，在SemEval数据集上超越了大部分方法且当加入弱监督时达到了最优效果。

双向条件编码的立场检测