MITRE at SemEval 2016 Task 6: Transfer Learning for Stance Detection
We describe MITRE's submission to the SemEval 2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.
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