@inproceedings{nekvinda-dusek-2022-aargh,
title = "{AARGH}! End-to-end Retrieval-Generation for Task-Oriented Dialog",
author = "Nekvinda, Tom{\'a}{\v{s}} and
Du{\v{s}}ek, Ond{\v{r}}ej",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.29",
doi = "10.18653/v1/2022.sigdial-1.29",
pages = "283--297",
abstract = "We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.",
}
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%0 Conference Proceedings
%T AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog
%A Nekvinda, Tomáš
%A Dušek, Ondřej
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F nekvinda-dusek-2022-aargh
%X We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.
%R 10.18653/v1/2022.sigdial-1.29
%U https://aclanthology.org/2022.sigdial-1.29
%U https://doi.org/10.18653/v1/2022.sigdial-1.29
%P 283-297
Markdown (Informal)
[AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog](https://aclanthology.org/2022.sigdial-1.29) (Nekvinda & Dušek, SIGDIAL 2022)
ACL