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arXiv · 1606.03784

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

Guido Zarrella·Amy Marsh
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Abstract

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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Guido Zarrella, Amy Marsh. 2016-06-13. MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection. https://arxiv.org/abs/1606.03784

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