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    <abstract>When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model Optimus (Organizing sentences via Pre-Trained Modeling of a Universal Space). A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks.</abstract>
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%0 Conference Proceedings
%T Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
%A Li, Chunyuan
%A Gao, Xiang
%A Li, Yuan
%A Peng, Baolin
%A Li, Xiujun
%A Zhang, Yizhe
%A Gao, Jianfeng
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-optimus
%X When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model Optimus (Organizing sentences via Pre-Trained Modeling of a Universal Space). A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks.
%R 10.18653/v1/2020.emnlp-main.378
%U https://aclanthology.org/2020.emnlp-main.378
%U https://doi.org/10.18653/v1/2020.emnlp-main.378
%P 4678-4699

Markdown (Informal)

[Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://aclanthology.org/2020.emnlp-main.378) (Li et al., EMNLP 2020)

• Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (Li et al., EMNLP 2020)

ACL

• Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, and Jianfeng Gao. 2020. Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4678–4699, Online. Association for Computational Linguistics.