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.- Anthology ID:
- W17-3526
- Volume:
- Proceedings of the 10th International Conference on Natural Language Generation
- Month:
- September
- Year:
- 2017
- Address:
- Santiago de Compostela, Spain
- Editors:
- Jose M. Alonso, Alberto Bugarín, Ehud Reiter
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 168–177
- Language:
- URL:
- https://aclanthology.org/W17-3526
- DOI:
- 10.18653/v1/W17-3526
- Cite (ACL):
- Hongyu Zang and Xiaojun Wan. 2017. Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores. In Proceedings of the 10th International Conference on Natural Language Generation, pages 168–177, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores (Zang & Wan, INLG 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-3526.pdf