Natural Language Generation by Hierarchical Decoding with Linguistic Patterns

Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, Yun-Nung Chen

Abstract
Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. 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.
Anthology ID:
N18-2010
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–66
Language:
URL:
https://aclanthology.org/N18-2010
DOI:
10.18653/v1/N18-2010
Bibkey:
Cite (ACL):
Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, and Yun-Nung Chen. 2018. Natural Language Generation by Hierarchical Decoding with Linguistic Patterns. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 61–66, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns (Su et al., NAACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/N18-2010.pdf
Code
 MiuLab/HNLG
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