@inproceedings{sato-etal-2021-speculative-sampling,
title = "Speculative Sampling in Variational Autoencoders for Dialogue Response Generation",
author = "Sato, Shoetsu and
Yoshinaga, Naoki and
Toyoda, Masashi and
Kitsuregawa, Masaru",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.407",
doi = "10.18653/v1/2021.findings-emnlp.407",
pages = "4739--4745",
abstract = "Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.",
}
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<abstract>Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.</abstract>
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%0 Conference Proceedings
%T Speculative Sampling in Variational Autoencoders for Dialogue Response Generation
%A Sato, Shoetsu
%A Yoshinaga, Naoki
%A Toyoda, Masashi
%A Kitsuregawa, Masaru
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F sato-etal-2021-speculative-sampling
%X Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.
%R 10.18653/v1/2021.findings-emnlp.407
%U https://aclanthology.org/2021.findings-emnlp.407
%U https://doi.org/10.18653/v1/2021.findings-emnlp.407
%P 4739-4745
Markdown (Informal)
[Speculative Sampling in Variational Autoencoders for Dialogue Response Generation](https://aclanthology.org/2021.findings-emnlp.407) (Sato et al., Findings 2021)
ACL