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        <title>Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning</title>
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    <name type="personal">
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    <abstract>Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for learning and inference. Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time. In this work, we explore the use of reinforcement learning to train effective sentence compression models that are also fast when generating predictions. In particular, we cast the task as binary sequence labelling and fine-tune a pre-trained transformer using a simple policy gradient approach. Our approach outperforms other unsupervised models while also being more efficient at inference time.</abstract>
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%0 Conference Proceedings
%T Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
%A Ghalandari, Demian
%A Hokamp, Chris
%A Ifrim, Georgiana
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ghalandari-etal-2022-efficient
%X Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for learning and inference. Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time. In this work, we explore the use of reinforcement learning to train effective sentence compression models that are also fast when generating predictions. In particular, we cast the task as binary sequence labelling and fine-tune a pre-trained transformer using a simple policy gradient approach. Our approach outperforms other unsupervised models while also being more efficient at inference time.
%R 10.18653/v1/2022.acl-long.90
%U https://aclanthology.org/2022.acl-long.90
%U https://doi.org/10.18653/v1/2022.acl-long.90
%P 1267-1280

Markdown (Informal)

[Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning](https://aclanthology.org/2022.acl-long.90) (Ghalandari et al., ACL 2022)

• Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning (Ghalandari et al., ACL 2022)

ACL

• Demian Ghalandari, Chris Hokamp, and Georgiana Ifrim. 2022. Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1267–1280, Dublin, Ireland. Association for Computational Linguistics.