@inproceedings{srivatsa-etal-2021-simplener,
title = "{S}imple{NER} Sentence Simplification System for {GEM} 2021",
author = "Srivatsa, K V Aditya and
Gokani, Monil and
Shrivastava, Manish",
editor = "Bosselut, Antoine and
Durmus, Esin and
Gangal, Varun Prashant and
Gehrmann, Sebastian and
Jernite, Yacine and
Perez-Beltrachini, Laura and
Shaikh, Samira and
Xu, Wei",
booktitle = "Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.gem-1.14",
doi = "10.18653/v1/2021.gem-1.14",
pages = "155--160",
abstract = "This paper describes SimpleNER, a model developed for the sentence simplification task at GEM-2021. Our system is a monolingual Seq2Seq Transformer architecture that uses control tokens pre-pended to the data, allowing the model to shape the generated simplifications according to user desired attributes. Additionally, we show that NER-tagging the training data before use helps stabilize the effect of the control tokens and significantly improves the overall performance of the system. We also employ pretrained embeddings to reduce data sparsity and allow the model to produce more generalizable outputs.",
}
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%0 Conference Proceedings
%T SimpleNER Sentence Simplification System for GEM 2021
%A Srivatsa, K. V. Aditya
%A Gokani, Monil
%A Shrivastava, Manish
%Y Bosselut, Antoine
%Y Durmus, Esin
%Y Gangal, Varun Prashant
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Perez-Beltrachini, Laura
%Y Shaikh, Samira
%Y Xu, Wei
%S Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F srivatsa-etal-2021-simplener
%X This paper describes SimpleNER, a model developed for the sentence simplification task at GEM-2021. Our system is a monolingual Seq2Seq Transformer architecture that uses control tokens pre-pended to the data, allowing the model to shape the generated simplifications according to user desired attributes. Additionally, we show that NER-tagging the training data before use helps stabilize the effect of the control tokens and significantly improves the overall performance of the system. We also employ pretrained embeddings to reduce data sparsity and allow the model to produce more generalizable outputs.
%R 10.18653/v1/2021.gem-1.14
%U https://aclanthology.org/2021.gem-1.14
%U https://doi.org/10.18653/v1/2021.gem-1.14
%P 155-160
Markdown (Informal)
[SimpleNER Sentence Simplification System for GEM 2021](https://aclanthology.org/2021.gem-1.14) (Srivatsa et al., GEM 2021)
ACL
- K V Aditya Srivatsa, Monil Gokani, and Manish Shrivastava. 2021. SimpleNER Sentence Simplification System for GEM 2021. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 155–160, Online. Association for Computational Linguistics.