@inproceedings{faltings-etal-2023-interactive,
title = "Interactive Text Generation",
author = "Faltings, Felix and
Galley, Michel and
Brantley, Kiant{\'e} and
Peng, Baolin and
Cai, Weixin and
Zhang, Yizhe and
Gao, Jianfeng and
Dolan, Bill",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.270",
doi = "10.18653/v1/2023.emnlp-main.270",
pages = "4450--4468",
abstract = "Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.",
}
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<abstract>Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.</abstract>
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%0 Conference Proceedings
%T Interactive Text Generation
%A Faltings, Felix
%A Galley, Michel
%A Brantley, Kianté
%A Peng, Baolin
%A Cai, Weixin
%A Zhang, Yizhe
%A Gao, Jianfeng
%A Dolan, Bill
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F faltings-etal-2023-interactive
%X Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.
%R 10.18653/v1/2023.emnlp-main.270
%U https://aclanthology.org/2023.emnlp-main.270
%U https://doi.org/10.18653/v1/2023.emnlp-main.270
%P 4450-4468
Markdown (Informal)
[Interactive Text Generation](https://aclanthology.org/2023.emnlp-main.270) (Faltings et al., EMNLP 2023)
ACL
- Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, and Bill Dolan. 2023. Interactive Text Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4450–4468, Singapore. Association for Computational Linguistics.