@inproceedings{qiu-etal-2018-transfer,
title = "Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in {E}-commerce",
author = "Qiu, Minghui and
Yang, Liu and
Ji, Feng and
Zhou, Wei and
Huang, Jun and
Chen, Haiqing and
Croft, Bruce and
Lin, Wei",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2034",
doi = "10.18653/v1/P18-2034",
pages = "208--213",
abstract = "Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist and observed a significant improvement over the existing online model.",
}
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<abstract>Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist and observed a significant improvement over the existing online model.</abstract>
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%0 Conference Proceedings
%T Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
%A Qiu, Minghui
%A Yang, Liu
%A Ji, Feng
%A Zhou, Wei
%A Huang, Jun
%A Chen, Haiqing
%A Croft, Bruce
%A Lin, Wei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F qiu-etal-2018-transfer
%X Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist and observed a significant improvement over the existing online model.
%R 10.18653/v1/P18-2034
%U https://aclanthology.org/P18-2034
%U https://doi.org/10.18653/v1/P18-2034
%P 208-213
Markdown (Informal)
[Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce](https://aclanthology.org/P18-2034) (Qiu et al., ACL 2018)
ACL
- Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, and Wei Lin. 2018. Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 208–213, Melbourne, Australia. Association for Computational Linguistics.