Controllable Text Simplification with Lexical Constraint Loss

Daiki Nishihara, Tomoyuki Kajiwara, Yuki Arase


Abstract
We propose a method to control the level of a sentence in a text simplification task. Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alternative. In this study, we use the grade level of the US education system as the level of the sentence. Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words. Sentence level is considered by adding the target grade level as input. By contrast, the word level is considered by adding weights to the training loss based on words that frequently appear in sentences of the desired grade level. Although existing models that consider only the sentence level may control the syntactic complexity, they tend to generate words beyond the target level. Our approach can control both the lexical and syntactic complexity and achieve an aggressive rewriting. Experiment results indicate that the proposed method improves the metrics of both BLEU and SARI.
Anthology ID:
P19-2036
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
260–266
Language:
URL:
https://aclanthology.org/P19-2036
DOI:
10.18653/v1/P19-2036
Bibkey:
Cite (ACL):
Daiki Nishihara, Tomoyuki Kajiwara, and Yuki Arase. 2019. Controllable Text Simplification with Lexical Constraint Loss. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 260–266, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Controllable Text Simplification with Lexical Constraint Loss (Nishihara et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-2036.pdf
Data
Newsela