@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T YNU_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble
%A Liu, Qingxun
%A Yao, Hongdou
%A Zhou, Xaobing
%A Xie, Ge
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F liu-etal-2018-ynu
%X 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.
%R 10.18653/v1/S18-1173
%U https://aclanthology.org/S18-1173
%U https://doi.org/10.18653/v1/S18-1173
%P 1038-1042
Markdown (Informal)
[YNU_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble](https://aclanthology.org/S18-1173) (Liu et al., SemEval 2018)
ACL