YNU_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble
Qingxun Liu, Hongdou Yao, Zhou Xaobing, Xie Ge · 2018
In this paper, we describe a machine reading comprehension system that participated in SemEval-2018 Task 11: Machine Comprehension using commonsense knowledge.In this work, we train a series of neural network models such as multi-LSTM, BiLSTM, multi-BiLSTM-CNN and attention-based BiLSTM, etc.On top of some sub models, there are two kinds of word embedding: (a) general word embedding generated from unsupervised neural language model; and (b) position embedding generated from general word embedding.Finally, we make a hard vote on the predictions of these models and achieve relatively good result.The proposed approach achieves 8th place in Task 11 with the accuracy of 0.7213.