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        <title>Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning</title>
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    <abstract>Previously, non-autoregressive models were widely recognized as being superior in generation efficiency but inferior in generation quality due to the challenges of modeling multiple target modalities.To enhance the multi-modality modeling ability, we propose the diffusion glancing transformer, which employs a modality diffusion process and residual glancing sampling.The modality diffusion process is a discrete process that interpolates the multi-modal distribution along the decoding steps, and the residual glancing sampling approach guides the model to continuously learn the remaining modalities across the layers. Experimental results on various machine translation and text generation benchmarks demonstrate that DIFFGLAT achieves better generation accuracy while maintaining fast decoding speed compared with both autoregressive and non-autoregressive models.</abstract>
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%0 Conference Proceedings
%T Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning
%A Qian, Lihua
%A Wang, Mingxuan
%A Liu, Yang
%A Zhou, Hao
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F qian-etal-2024-diffusion
%X Previously, non-autoregressive models were widely recognized as being superior in generation efficiency but inferior in generation quality due to the challenges of modeling multiple target modalities.To enhance the multi-modality modeling ability, we propose the diffusion glancing transformer, which employs a modality diffusion process and residual glancing sampling.The modality diffusion process is a discrete process that interpolates the multi-modal distribution along the decoding steps, and the residual glancing sampling approach guides the model to continuously learn the remaining modalities across the layers. Experimental results on various machine translation and text generation benchmarks demonstrate that DIFFGLAT achieves better generation accuracy while maintaining fast decoding speed compared with both autoregressive and non-autoregressive models.
%U https://aclanthology.org/2024.naacl-long.271
%P 4846-4862

Markdown (Informal)

[Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning](https://aclanthology.org/2024.naacl-long.271) (Qian et al., NAACL 2024)

• Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (Qian et al., NAACL 2024)

ACL

• Lihua Qian, Mingxuan Wang, Yang Liu, and Hao Zhou. 2024. Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4846–4862, Mexico City, Mexico. Association for Computational Linguistics.