Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

Amit Moryossef, Yoav Goldberg, Ido Dagan

Abstract
Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system’s reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.
Anthology ID:
N19-1236
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2267–2277
Language:
URL:
https://aclanthology.org/N19-1236
DOI:
10.18653/v1/N19-1236
Bibkey:
Cite (ACL):
Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019. Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2267–2277, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (Moryossef et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/N19-1236.pdf
Supplementary:
 N19-1236.Supplementary.pdf
Code
 AmitMY/chimera
Data
WebNLG
PDF Search Code Supplementary