@inproceedings{zang-wan-2017-towards,
title = "Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores",
author = "Zang, Hongyu and
Wan, Xiaojun",
editor = "Alonso, Jose M. and
Bugar{\'\i}n, Alberto and
Reiter, Ehud",
booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
month = sep,
year = "2017",
address = "Santiago de Compostela, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3526",
doi = "10.18653/v1/W17-3526",
pages = "168--177",
abstract = "Data-to-text generation is very essential and important in machine writing applications. The recent deep learning models, like Recurrent Neural Networks (RNNs), have shown a bright future for relevant text generation tasks. However, rare work has been done for automatic generation of long reviews from user opinions. In this paper, we introduce a deep neural network model to generate long Chinese reviews from aspect-sentiment scores representing users{'} opinions. We conduct our study within the framework of encoder-decoder networks, and we propose a hierarchical structure with aligned attention in the Long-Short Term Memory (LSTM) decoder. Experiments show that our model outperforms retrieval based baseline methods, and also beats the sequential generation models in qualitative evaluations.",
}
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%0 Conference Proceedings
%T Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores
%A Zang, Hongyu
%A Wan, Xiaojun
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F zang-wan-2017-towards
%X Data-to-text generation is very essential and important in machine writing applications. The recent deep learning models, like Recurrent Neural Networks (RNNs), have shown a bright future for relevant text generation tasks. However, rare work has been done for automatic generation of long reviews from user opinions. In this paper, we introduce a deep neural network model to generate long Chinese reviews from aspect-sentiment scores representing users’ opinions. We conduct our study within the framework of encoder-decoder networks, and we propose a hierarchical structure with aligned attention in the Long-Short Term Memory (LSTM) decoder. Experiments show that our model outperforms retrieval based baseline methods, and also beats the sequential generation models in qualitative evaluations.
%R 10.18653/v1/W17-3526
%U https://aclanthology.org/W17-3526
%U https://doi.org/10.18653/v1/W17-3526
%P 168-177
Markdown (Informal)
[Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores](https://aclanthology.org/W17-3526) (Zang & Wan, INLG 2017)
ACL