@inproceedings{saini-etal-2024-spivavtor,
title = "Spivavtor: An Instruction Tuned {U}krainian Text Editing Model",
author = "Saini, Aman and
Chernodub, Artem and
Raheja, Vipul and
Kulkarni, Vivek",
editor = "Romanyshyn, Mariana and
Romanyshyn, Nataliia and
Hlybovets, Andrii and
Ignatenko, Oleksii",
booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.unlp-1.12",
pages = "95--108",
abstract = "We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT (Raheja et al., 2023) model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian like {``}Виправте граматику в цьому реченнi{''} and {``}Спростiть це речення{''} which translate to {``}Correct the grammar in this sentence{''} and {``}Simplify this sentence{''} in English, respectively. This paper describes the details of the Spivavtor-Instruct dataset and Spivavtor models. We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as Grammatical Error Correction (GEC), Text Simplification, Coherence, and Paraphrasing, and demonstrate its superior performance on all of them. We publicly release our best performing models and data as resources to the community to advance further research in this space.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="saini-etal-2024-spivavtor">
<titleInfo>
<title>Spivavtor: An Instruction Tuned Ukrainian Text Editing Model</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aman</namePart>
<namePart type="family">Saini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Artem</namePart>
<namePart type="family">Chernodub</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vipul</namePart>
<namePart type="family">Raheja</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Kulkarni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mariana</namePart>
<namePart type="family">Romanyshyn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nataliia</namePart>
<namePart type="family">Romanyshyn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrii</namePart>
<namePart type="family">Hlybovets</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oleksii</namePart>
<namePart type="family">Ignatenko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT (Raheja et al., 2023) model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian like “Виправте граматику в цьому реченнi” and “Спростiть це речення” which translate to “Correct the grammar in this sentence” and “Simplify this sentence” in English, respectively. This paper describes the details of the Spivavtor-Instruct dataset and Spivavtor models. We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as Grammatical Error Correction (GEC), Text Simplification, Coherence, and Paraphrasing, and demonstrate its superior performance on all of them. We publicly release our best performing models and data as resources to the community to advance further research in this space.</abstract>
<identifier type="citekey">saini-etal-2024-spivavtor</identifier>
<location>
<url>https://aclanthology.org/2024.unlp-1.12</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>95</start>
<end>108</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
%A Saini, Aman
%A Chernodub, Artem
%A Raheja, Vipul
%A Kulkarni, Vivek
%Y Romanyshyn, Mariana
%Y Romanyshyn, Nataliia
%Y Hlybovets, Andrii
%Y Ignatenko, Oleksii
%S Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F saini-etal-2024-spivavtor
%X We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT (Raheja et al., 2023) model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian like “Виправте граматику в цьому реченнi” and “Спростiть це речення” which translate to “Correct the grammar in this sentence” and “Simplify this sentence” in English, respectively. This paper describes the details of the Spivavtor-Instruct dataset and Spivavtor models. We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as Grammatical Error Correction (GEC), Text Simplification, Coherence, and Paraphrasing, and demonstrate its superior performance on all of them. We publicly release our best performing models and data as resources to the community to advance further research in this space.
%U https://aclanthology.org/2024.unlp-1.12
%P 95-108
Markdown (Informal)
[Spivavtor: An Instruction Tuned Ukrainian Text Editing Model](https://aclanthology.org/2024.unlp-1.12) (Saini et al., UNLP 2024)
ACL