This Word Mean What: Constructing a Singlish Dictionary with ChatGPT

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F24%3A73628983" target="_blank" >RIV/61989592:15210/24:73628983 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.eurali-1.7.pdf" target="_blank" >https://aclanthology.org/2024.eurali-1.7.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

This Word Mean What: Constructing a Singlish Dictionary with ChatGPT

Popis výsledku v původním jazyce

Despite the magnitude of recent progress in natural language processing and multilingual language modeling research, the vast majority of NLP research is focused on English and other major languages. This is because recent NLP research is mainly data-driven, and there is more data for resource-rich languages. In particular, Large Language Models (LLM) make use of large unlabeled datasets, a resource that many languages do not have. In this project, we built a new, open-sourced dictionary of Singlish, a contact variety that contains features from English and other local languages and is syntactically, phonologically and lexically distinct from Standard English (Tan 2010). First, a list of Singlish words was extracted from various online sources. Then using an open Chat-GPT LLM API, the description, including the defintion, part of speech, pronunciation and examples was produced. These were then refined through post processing carried out by a native speaker. The dictionary currently has 1,783 entries and is published under the CC-BY-SA license. The project was carried out with the intention of facilitating future Singlish research and other applications as the accumulation and management of language resources will be of great help in promoting research on the language in the future.

Název v anglickém jazyce

This Word Mean What: Constructing a Singlish Dictionary with ChatGPT

Popis výsledku anglicky

Despite the magnitude of recent progress in natural language processing and multilingual language modeling research, the vast majority of NLP research is focused on English and other major languages. This is because recent NLP research is mainly data-driven, and there is more data for resource-rich languages. In particular, Large Language Models (LLM) make use of large unlabeled datasets, a resource that many languages do not have. In this project, we built a new, open-sourced dictionary of Singlish, a contact variety that contains features from English and other local languages and is syntactically, phonologically and lexically distinct from Standard English (Tan 2010). First, a list of Singlish words was extracted from various online sources. Then using an open Chat-GPT LLM API, the description, including the defintion, part of speech, pronunciation and examples was produced. These were then refined through post processing carried out by a native speaker. The dictionary currently has 1,783 entries and is published under the CC-BY-SA license. The project was carried out with the intention of facilitating future Singlish research and other applications as the accumulation and management of language resources will be of great help in promoting research on the language in the future.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60203 - Linguistics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024

ISBN

978-2-493-81433-3

ISSN

2522-2686

e-ISSN

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Počet stran výsledku

10

Strana od-do

"41–50"

Název nakladatele

ELRA Language Resources Association

Místo vydání

Turin

Místo konání akce

Turin

Datum konání akce

20. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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