@inproceedings{ippolito-etal-2022-case,
title = "The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank",
author = "Ippolito, Daphne and
Dugan, Liam and
Reif, Emily and
Yuan, Ann and
Coenen, Andy and
Callison-Burch, Chris",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.185",
doi = "10.18653/v1/2022.findings-naacl.185",
pages = "2421--2432",
abstract = "The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform {\_}both{\_} FitB and continuation tasks. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how these models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.",
}
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<abstract>The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how these models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.</abstract>
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%0 Conference Proceedings
%T The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank
%A Ippolito, Daphne
%A Dugan, Liam
%A Reif, Emily
%A Yuan, Ann
%A Coenen, Andy
%A Callison-Burch, Chris
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F ippolito-etal-2022-case
%X The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how these models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.
%R 10.18653/v1/2022.findings-naacl.185
%U https://aclanthology.org/2022.findings-naacl.185
%U https://doi.org/10.18653/v1/2022.findings-naacl.185
%P 2421-2432
Markdown (Informal)
[The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank](https://aclanthology.org/2022.findings-naacl.185) (Ippolito et al., Findings 2022)
ACL