@inproceedings{roush-etal-2022-language,
title = "Most Language Models can be Poets too: An {AI} Writing Assistant and Constrained Text Generation Studio",
author = "Roush, Allen and
Basu, Sanjay and
Moorthy, Akshay and
Dubovoy, Dmitry",
editor = "Wu, Xianchao and
Ruan, Peiying and
Li, Sheng and
Dong, Yi",
booktitle = "Proceedings of the Second Workshop on When Creative AI Meets Conversational AI",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.cai-1.2",
pages = "9--15",
abstract = "Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under significant constraints. We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called {``}Constrained Text Generation Studio{''} (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word. We introduce a novel dataset of prose that omits the letter {``}e{''}. We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset. We also present a Huggingface {``}space{''} web-app presenting this technique called Gadsby. The code is available to the public here: \url{https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio}",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="roush-etal-2022-language">
<titleInfo>
<title>Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio</title>
</titleInfo>
<name type="personal">
<namePart type="given">Allen</namePart>
<namePart type="family">Roush</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sanjay</namePart>
<namePart type="family">Basu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Akshay</namePart>
<namePart type="family">Moorthy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dmitry</namePart>
<namePart type="family">Dubovoy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on When Creative AI Meets Conversational AI</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xianchao</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peiying</namePart>
<namePart type="family">Ruan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sheng</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="family">Dong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under significant constraints. We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called “Constrained Text Generation Studio” (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word. We introduce a novel dataset of prose that omits the letter “e”. We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset. We also present a Huggingface “space” web-app presenting this technique called Gadsby. The code is available to the public here: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio</abstract>
<identifier type="citekey">roush-etal-2022-language</identifier>
<location>
<url>https://aclanthology.org/2022.cai-1.2</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>9</start>
<end>15</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio
%A Roush, Allen
%A Basu, Sanjay
%A Moorthy, Akshay
%A Dubovoy, Dmitry
%Y Wu, Xianchao
%Y Ruan, Peiying
%Y Li, Sheng
%Y Dong, Yi
%S Proceedings of the Second Workshop on When Creative AI Meets Conversational AI
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F roush-etal-2022-language
%X Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under significant constraints. We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called “Constrained Text Generation Studio” (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word. We introduce a novel dataset of prose that omits the letter “e”. We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset. We also present a Huggingface “space” web-app presenting this technique called Gadsby. The code is available to the public here: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio
%U https://aclanthology.org/2022.cai-1.2
%P 9-15
Markdown (Informal)
[Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio](https://aclanthology.org/2022.cai-1.2) (Roush et al., CAI 2022)
ACL