Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF

Yang Xiang, Xiaoqiang Zhou, Qingcai Chen, Zhihui Zheng, Buzhou Tang, Xiaolong Wang, Yang Qin

[How to correct problems with metadata yourself]


Abstract
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Net-works, Long Short Term Memory and Conditional Random Fields. Extensive experi-ments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96% on macro averaged F1, which improves the state-of-the-art neural network-based method by 2.82% and outper-forms the Top-1 system in the shared task by 1.77%. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29% on overall F1 and gains the best performance on the Good and Bad categories.
Anthology ID:
C16-1117
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1231–1241
Language:
URL:
https://aclanthology.org/C16-1117
DOI:
Bibkey:
Cite (ACL):
Yang Xiang, Xiaoqiang Zhou, Qingcai Chen, Zhihui Zheng, Buzhou Tang, Xiaolong Wang, and Yang Qin. 2016. Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1231–1241, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF (Xiang et al., COLING 2016)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C16-1117.pdf
Code
 o0laika0o/CNN-LSTM-CRF-for-cQA-answer-tagging