CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-03-29
Cited: 0
Clicked: 6707
Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li. Attention-based encoder-decoder model for answer selection in question answering[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 535-544.
@article{title="Attention-based encoder-decoder model for answer selection in question answering",
author="Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="4",
pages="535-544",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601232"
}
%0 Journal Article
%T Attention-based encoder-decoder model for answer selection in question answering
%A Yuan-ping Nie
%A Yi Han
%A Jiu-ming Huang
%A Bo Jiao
%A Ai-ping Li
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 4
%P 535-544
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601232
TY - JOUR
T1 - Attention-based encoder-decoder model for answer selection in question answering
A1 - Yuan-ping Nie
A1 - Yi Han
A1 - Jiu-ming Huang
A1 - Bo Jiao
A1 - Ai-ping Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 4
SP - 535
EP - 544
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601232
Abstract: One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.
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