@inproceedings{liu-etal-2019-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2019 Task 8: Using A {LSTM}-Attention Model for Fact-Checking in Community Forums",
author = "Liu, Peng and
Wang, Jin and
Zhang, Xuejie",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2207",
doi = "10.18653/v1/S19-2207",
pages = "1180--1184",
abstract = "We propose a system that uses a long short-term memory with attention mechanism (LSTM-Attention) model to complete the task. The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair. Then, each of the features is sequentially composed using the attention mechanism, concatenating the two vectors into one. Finally, the concatenated vector is used as input for the MLP and the MLP{'}s output layer uses the softmax function to classify the provided answers into three categories. This model is capable of extracting the features of the question and answer pair well. The results show that the proposed system outperforms the baseline algorithm.",
}
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums
%A Liu, Peng
%A Wang, Jin
%A Zhang, Xuejie
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F liu-etal-2019-ynu
%X We propose a system that uses a long short-term memory with attention mechanism (LSTM-Attention) model to complete the task. The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair. Then, each of the features is sequentially composed using the attention mechanism, concatenating the two vectors into one. Finally, the concatenated vector is used as input for the MLP and the MLP’s output layer uses the softmax function to classify the provided answers into three categories. This model is capable of extracting the features of the question and answer pair well. The results show that the proposed system outperforms the baseline algorithm.
%R 10.18653/v1/S19-2207
%U https://aclanthology.org/S19-2207
%U https://doi.org/10.18653/v1/S19-2207
%P 1180-1184
Markdown (Informal)
[YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums](https://aclanthology.org/S19-2207) (Liu et al., SemEval 2019)
ACL