@inproceedings{liu-etal-2018-ynu,
    title = "{YNU}{\_}{AI}1799 at {S}em{E}val-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble",
    author = "Liu, Qingxun  and
      Yao, Hongdou  and
      Zhou, Xaobing  and
      Xie, Ge",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-1173/",
    doi = "10.18653/v1/S18-1173",
    pages = "1038--1042",
    abstract = "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."
}