This paper is published in Proceedings of The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018).
Code: GitHub
Cite the paper:
@inproceedings{su2018natural,
title={Natural Language Generation by Hierarchical Decoding with Linguistic Patterns},
author={Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, and Yun-Nung Chen},
booktitle={Proceedings of The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2018}
}
Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases:
Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains a encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion.
However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge.
This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size. Furthermore, the design of the hierarchical decoding is flexible and easily-extendible in various NLG systems (The source code is available at https://github.com/MiuLab/HNLG).