In this paper, we develop a convolutional neural network for stance detection in tweets. According to the official results, our system ranks 1 st on subtask B (among 9 teams) and ranks 2 nd on subtask A (among 19 teams) on the twitter test set of SemEval2016 Task 6. The main contribution of our work is as follows. We design a "vote scheme" for prediction instead of predicting when the accuracy of validation set reaches its maximum. Besides, we make some improvement on the specific subtasks. For subtask A, we separate datasets into five sub-datasets according to their targets, and train and test five separate models. For subtask B, we establish a two-class training dataset from the official domain corpus, and then modify the softmax layer to perform three-class classification. Our system can be easily re-implemented and optimized for other related tasks.
Also in the context of the SemEval-2016, the work in REF presents an approach based on convolutional neural networks that, instead of simply predicting when the validation accuracy will reach its maximum, uses a voting scheme and other secondary improvements.
pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection
SemEval@NAACL-HLT
Computer Science