MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella, Amy Marsh · 2016
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 finetuned 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.