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We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stancein Tweets. This effort achieved the top score in Task A on supervised stancedetection, producing an average F1 score of 67.8 when assessing whether a tweetauthor was in favor or against a topic. We employed a recurrent neural networkinitialized with features learned via distant supervision on two largeunlabeled datasets. We trained embeddings of words and phrases with theword2vec skip-gram method, then used those features to learn sentencerepresentations via a hashtag prediction auxiliary task. These sentence vectorswere then fine-tuned for stance detection on several hundred labeled examples.The result was a high performing system that used transfer learning to maximizethe value of the available training data.

MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection