@inproceedings{romeo-etal-2016-neural,
title = "Neural Attention for Learning to Rank Questions in Community Question Answering",
author = "Romeo, Salvatore and
Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Moschitti, Alessandro and
Belinkov, Yonatan and
Hsu, Wei-Ning and
Zhang, Yu and
Mohtarami, Mitra and
Glass, James",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1163",
pages = "1734--1745",
abstract = "In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.",
}
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<abstract>In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.</abstract>
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%0 Conference Proceedings
%T Neural Attention for Learning to Rank Questions in Community Question Answering
%A Romeo, Salvatore
%A Da San Martino, Giovanni
%A Barrón-Cedeño, Alberto
%A Moschitti, Alessandro
%A Belinkov, Yonatan
%A Hsu, Wei-Ning
%A Zhang, Yu
%A Mohtarami, Mitra
%A Glass, James
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F romeo-etal-2016-neural
%X In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.
%U https://aclanthology.org/C16-1163
%P 1734-1745
Markdown (Informal)
[Neural Attention for Learning to Rank Questions in Community Question Answering](https://aclanthology.org/C16-1163) (Romeo et al., COLING 2016)
ACL
- Salvatore Romeo, Giovanni Da San Martino, Alberto Barrón-Cedeño, Alessandro Moschitti, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Mitra Mohtarami, and James Glass. 2016. Neural Attention for Learning to Rank Questions in Community Question Answering. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1734–1745, Osaka, Japan. The COLING 2016 Organizing Committee.