@inproceedings{mehri-etal-2022-lad,
title = "{LAD}: Language Models as Data for Zero-Shot Dialog",
author = "Mehri, Shikib and
Altun, Yasemin and
Eskenazi, Maxine",
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-1.55",
doi = "10.18653/v1/2022.sigdial-1.55",
pages = "595--604",
abstract = "To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15{\%}), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.",
}
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<abstract>To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.</abstract>
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%0 Conference Proceedings
%T LAD: Language Models as Data for Zero-Shot Dialog
%A Mehri, Shikib
%A Altun, Yasemin
%A Eskenazi, Maxine
%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 mehri-etal-2022-lad
%X To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.
%R 10.18653/v1/2022.sigdial-1.55
%U https://aclanthology.org/2022.sigdial-1.55
%U https://doi.org/10.18653/v1/2022.sigdial-1.55
%P 595-604
Markdown (Informal)
[LAD: Language Models as Data for Zero-Shot Dialog](https://aclanthology.org/2022.sigdial-1.55) (Mehri et al., SIGDIAL 2022)
ACL
- Shikib Mehri, Yasemin Altun, and Maxine Eskenazi. 2022. LAD: Language Models as Data for Zero-Shot Dialog. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 595–604, Edinburgh, UK. Association for Computational Linguistics.