@inproceedings{kim-etal-2024-mild,
title = "{MILD} Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot",
author = "Kim, Mirae and
Hwang, Kyubum and
Oh, Hayoung and
Kim, Min Ah and
Park, Chaerim and
Park, Yehwi and
Lee, Chungyeon",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.49",
pages = "665--676",
abstract = "This study introduces a Multidisciplinary chILDhood cancer survivor question-answering (MILD) bot designed to support childhood cancer survivors facing diverse challenges in their survivorship journey. In South Korea, a shortage of experts equipped to address these unique concerns comprehensively leaves survivors with limited access to reliable information. To bridge this gap, our MILD bot employs a dual-component model featuring an intent classifier and a semantic textual similarity model. The intent classifier first analyzes the user{'}s query to identify the underlying intent and match it with the most suitable expert who can provide advice. Then, the semantic textual similarity model identifies questions in a predefined dataset that closely align with the user{'}s query, ensuring the delivery of relevant responses. This proposed framework shows significant promise in offering timely, accurate, and high-quality information, effectively addressing a critical need for support among childhood cancer survivors.",
}
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<abstract>This study introduces a Multidisciplinary chILDhood cancer survivor question-answering (MILD) bot designed to support childhood cancer survivors facing diverse challenges in their survivorship journey. In South Korea, a shortage of experts equipped to address these unique concerns comprehensively leaves survivors with limited access to reliable information. To bridge this gap, our MILD bot employs a dual-component model featuring an intent classifier and a semantic textual similarity model. The intent classifier first analyzes the user’s query to identify the underlying intent and match it with the most suitable expert who can provide advice. Then, the semantic textual similarity model identifies questions in a predefined dataset that closely align with the user’s query, ensuring the delivery of relevant responses. This proposed framework shows significant promise in offering timely, accurate, and high-quality information, effectively addressing a critical need for support among childhood cancer survivors.</abstract>
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%0 Conference Proceedings
%T MILD Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot
%A Kim, Mirae
%A Hwang, Kyubum
%A Oh, Hayoung
%A Kim, Min Ah
%A Park, Chaerim
%A Park, Yehwi
%A Lee, Chungyeon
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F kim-etal-2024-mild
%X This study introduces a Multidisciplinary chILDhood cancer survivor question-answering (MILD) bot designed to support childhood cancer survivors facing diverse challenges in their survivorship journey. In South Korea, a shortage of experts equipped to address these unique concerns comprehensively leaves survivors with limited access to reliable information. To bridge this gap, our MILD bot employs a dual-component model featuring an intent classifier and a semantic textual similarity model. The intent classifier first analyzes the user’s query to identify the underlying intent and match it with the most suitable expert who can provide advice. Then, the semantic textual similarity model identifies questions in a predefined dataset that closely align with the user’s query, ensuring the delivery of relevant responses. This proposed framework shows significant promise in offering timely, accurate, and high-quality information, effectively addressing a critical need for support among childhood cancer survivors.
%U https://aclanthology.org/2024.emnlp-industry.49
%P 665-676
Markdown (Informal)
[MILD Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot](https://aclanthology.org/2024.emnlp-industry.49) (Kim et al., EMNLP 2024)
ACL
- Mirae Kim, Kyubum Hwang, Hayoung Oh, Min Ah Kim, Chaerim Park, Yehwi Park, and Chungyeon Lee. 2024. MILD Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 665–676, Miami, Florida, US. Association for Computational Linguistics.