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        <title>Sentence Bottleneck Autoencoders from Transformer Language Models</title>
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        <namePart type="given">Ivan</namePart>
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    <name type="personal">
        <namePart type="given">Nikolaos</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.137</identifier>
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%0 Conference Proceedings
%T Sentence Bottleneck Autoencoders from Transformer Language Models
%A Montero, Ivan
%A Pappas, Nikolaos
%A Smith, Noah A.
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F montero-etal-2021-sentence
%X Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
%R 10.18653/v1/2021.emnlp-main.137
%U https://aclanthology.org/2021.emnlp-main.137
%U https://doi.org/10.18653/v1/2021.emnlp-main.137
%P 1822-1831

Markdown (Informal)

[Sentence Bottleneck Autoencoders from Transformer Language Models](https://aclanthology.org/2021.emnlp-main.137) (Montero et al., EMNLP 2021)

• Sentence Bottleneck Autoencoders from Transformer Language Models (Montero et al., EMNLP 2021)

ACL

• Ivan Montero, Nikolaos Pappas, and Noah A. Smith. 2021. Sentence Bottleneck Autoencoders from Transformer Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1822–1831, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.