SINAI-DL at SemEval-2019 task 7: Data augmentation and temporal expressions

Abstract

This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labeling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.