@inproceedings{chanthran-etal-2023-well,
title = "How well {C}hat{GPT} understand {M}alaysian {E}nglish? An Evaluation on Named Entity Recognition and Relation Extraction",
author = "Chanthran, Mohanraj and
Soon, Lay-Ki and
Fang, Ong Huey and
Selvaretnam, Bhawani",
editor = "Gehrmann, Sebastian and
Wang, Alex and
Sedoc, Jo{\~a}o and
Clark, Elizabeth and
Dhole, Kaustubh and
Chandu, Khyathi Raghavi and
Santus, Enrico and
Sedghamiz, Hooman",
booktitle = "Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.gem-1.30",
pages = "372--397",
abstract = "Recently, ChatGPT has attracted a lot of interest from both researchers and the general public. While the performance of ChatGPT in Named Entity Recognition and Relation Extraction from Standard English texts is satisfactory, it remains to be seen if it can perform similarly for Malaysian English. Malaysian English is unique as it exhibits morphosyntactic and semantical adaptation from local contexts. In this study, we assess ChatGPT{'}s capability in extracting entities and relations from the Malaysian English News (MEN) dataset. We propose a three-step methodology referred to as \textbf{educate-predict-evaluate}. The performance of ChatGPT is assessed using F1-Score across 18 unique prompt settings, which were carefully engineered for a comprehensive review. From our evaluation, we found that ChatGPT does not perform well in extracting entities from Malaysian English news articles, with the highest F1-Score of 0.497. Further analysis shows that the morphosyntactic adaptation in Malaysian English caused the limitation. However, interestingly, this morphosyntactic adaptation does not impact the performance of ChatGPT for relation extraction.",
}
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<abstract>Recently, ChatGPT has attracted a lot of interest from both researchers and the general public. While the performance of ChatGPT in Named Entity Recognition and Relation Extraction from Standard English texts is satisfactory, it remains to be seen if it can perform similarly for Malaysian English. Malaysian English is unique as it exhibits morphosyntactic and semantical adaptation from local contexts. In this study, we assess ChatGPT’s capability in extracting entities and relations from the Malaysian English News (MEN) dataset. We propose a three-step methodology referred to as educate-predict-evaluate. The performance of ChatGPT is assessed using F1-Score across 18 unique prompt settings, which were carefully engineered for a comprehensive review. From our evaluation, we found that ChatGPT does not perform well in extracting entities from Malaysian English news articles, with the highest F1-Score of 0.497. Further analysis shows that the morphosyntactic adaptation in Malaysian English caused the limitation. However, interestingly, this morphosyntactic adaptation does not impact the performance of ChatGPT for relation extraction.</abstract>
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%0 Conference Proceedings
%T How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction
%A Chanthran, Mohanraj
%A Soon, Lay-Ki
%A Fang, Ong Huey
%A Selvaretnam, Bhawani
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chanthran-etal-2023-well
%X Recently, ChatGPT has attracted a lot of interest from both researchers and the general public. While the performance of ChatGPT in Named Entity Recognition and Relation Extraction from Standard English texts is satisfactory, it remains to be seen if it can perform similarly for Malaysian English. Malaysian English is unique as it exhibits morphosyntactic and semantical adaptation from local contexts. In this study, we assess ChatGPT’s capability in extracting entities and relations from the Malaysian English News (MEN) dataset. We propose a three-step methodology referred to as educate-predict-evaluate. The performance of ChatGPT is assessed using F1-Score across 18 unique prompt settings, which were carefully engineered for a comprehensive review. From our evaluation, we found that ChatGPT does not perform well in extracting entities from Malaysian English news articles, with the highest F1-Score of 0.497. Further analysis shows that the morphosyntactic adaptation in Malaysian English caused the limitation. However, interestingly, this morphosyntactic adaptation does not impact the performance of ChatGPT for relation extraction.
%U https://aclanthology.org/2023.gem-1.30
%P 372-397
Markdown (Informal)
[How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction](https://aclanthology.org/2023.gem-1.30) (Chanthran et al., GEM-WS 2023)
ACL