Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 2021
DOI: 10.18653/v1/2021.emnlp-main.483
|View full text |Cite
|
Sign up to set email alerts
|

IGA: An Intent-Guided Authoring Assistant

Abstract: While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored. We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases text according to fine-grained author specifications. Users provide input to our Intent-Guided Assistant (IGA) in the form of text interspersed with tags that correspond to specific rhetorical direc… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 23 publications
0
2
0
Order By: Relevance
“…Such generative tools, however, necessitate the right level of control [24]. While some have proposed to guide the generation with keywords [31,45,70,74], other works have proposed to use natural language prompts directly [27]. General conversational agents like ChatGPT [5] enable users to refine story generation through an iterative process and are now used by a wider audience [37].…”
Section: Story Assistantsmentioning
confidence: 99%
“…Such generative tools, however, necessitate the right level of control [24]. While some have proposed to guide the generation with keywords [31,45,70,74], other works have proposed to use natural language prompts directly [27]. General conversational agents like ChatGPT [5] enable users to refine story generation through an iterative process and are now used by a wider audience [37].…”
Section: Story Assistantsmentioning
confidence: 99%
“…Our work bears similarity to work on controllable story generation, which aims to control different attributes of stories such as the sentiments (Luo et al, 2019;Kong et al, 2021b), genres (Cho et al, 2022), intention (Sun et al, 2021), and characters (Lee and Jung, 2020;Xu et al, 2020a;Liu et al, 2020b) of stories. However, these attributes are largely unchanging throughout the story, while we focus on writing mode, a more dynamic attribute of text.…”
Section: Related Workmentioning
confidence: 99%