@inproceedings{chika-etal-2024-domain,
title = "Domain Transferable Semantic Frames for Expert Interview Dialogues",
author = "Chika, Taishi and
Okahisa, Taro and
Kodama, Takashi and
Huang, Yin Jou and
Murawaki, Yugo and
Kurohashi, Sadao",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.471/",
pages = "5299--5308",
abstract = "Interviews are an effective method to elicit critical skills to perform particular processes in various domains. In order to understand the knowledge structure of these domain-specific processes, we consider semantic role and predicate annotation based on Frame Semantics. We introduce a dataset of interview dialogues with experts in the culinary and gardening domains, each annotated with semantic frames. This dataset consists of (1) 308 interview dialogues related to the culinary domain, originally assembled by Okahisa et al. (2022), and (2) 100 interview dialogues associated with the gardening domain, which we newly acquired. The labeling specifications take into account the domain-transferability by adopting domain-agnostic labels for frame elements. In addition, we conducted domain transfer experiments from the culinary domain to the gardening domain to examine the domain transferability with our dataset. The experimental results showed the effectiveness of our domain-agnostic labeling scheme."
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<abstract>Interviews are an effective method to elicit critical skills to perform particular processes in various domains. In order to understand the knowledge structure of these domain-specific processes, we consider semantic role and predicate annotation based on Frame Semantics. We introduce a dataset of interview dialogues with experts in the culinary and gardening domains, each annotated with semantic frames. This dataset consists of (1) 308 interview dialogues related to the culinary domain, originally assembled by Okahisa et al. (2022), and (2) 100 interview dialogues associated with the gardening domain, which we newly acquired. The labeling specifications take into account the domain-transferability by adopting domain-agnostic labels for frame elements. In addition, we conducted domain transfer experiments from the culinary domain to the gardening domain to examine the domain transferability with our dataset. The experimental results showed the effectiveness of our domain-agnostic labeling scheme.</abstract>
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%0 Conference Proceedings
%T Domain Transferable Semantic Frames for Expert Interview Dialogues
%A Chika, Taishi
%A Okahisa, Taro
%A Kodama, Takashi
%A Huang, Yin Jou
%A Murawaki, Yugo
%A Kurohashi, Sadao
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F chika-etal-2024-domain
%X Interviews are an effective method to elicit critical skills to perform particular processes in various domains. In order to understand the knowledge structure of these domain-specific processes, we consider semantic role and predicate annotation based on Frame Semantics. We introduce a dataset of interview dialogues with experts in the culinary and gardening domains, each annotated with semantic frames. This dataset consists of (1) 308 interview dialogues related to the culinary domain, originally assembled by Okahisa et al. (2022), and (2) 100 interview dialogues associated with the gardening domain, which we newly acquired. The labeling specifications take into account the domain-transferability by adopting domain-agnostic labels for frame elements. In addition, we conducted domain transfer experiments from the culinary domain to the gardening domain to examine the domain transferability with our dataset. The experimental results showed the effectiveness of our domain-agnostic labeling scheme.
%U https://aclanthology.org/2024.lrec-main.471/
%P 5299-5308
Markdown (Informal)
[Domain Transferable Semantic Frames for Expert Interview Dialogues](https://aclanthology.org/2024.lrec-main.471/) (Chika et al., LREC-COLING 2024)
ACL
- Taishi Chika, Taro Okahisa, Takashi Kodama, Yin Jou Huang, Yugo Murawaki, and Sadao Kurohashi. 2024. Domain Transferable Semantic Frames for Expert Interview Dialogues. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5299–5308, Torino, Italia. ELRA and ICCL.