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    <abstract>Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/Sanqiang/text_simplification.</abstract>
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%0 Conference Proceedings
%T Integrating Transformer and Paraphrase Rules for Sentence Simplification
%A Zhao, Sanqiang
%A Meng, Rui
%A He, Daqing
%A Saptono, Andi
%A Parmanto, Bambang
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhao-etal-2018-integrating
%X Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/Sanqiang/text_simplification.
%R 10.18653/v1/D18-1355
%U https://aclanthology.org/D18-1355
%U https://doi.org/10.18653/v1/D18-1355
%P 3164-3173

Markdown (Informal)

[Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://aclanthology.org/D18-1355) (Zhao et al., EMNLP 2018)

• Integrating Transformer and Paraphrase Rules for Sentence Simplification (Zhao et al., EMNLP 2018)

ACL

• Sanqiang Zhao, Rui Meng, Daqing He, Andi Saptono, and Bambang Parmanto. 2018. Integrating Transformer and Paraphrase Rules for Sentence Simplification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3164–3173, Brussels, Belgium. Association for Computational Linguistics.