Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets

Farhad Akhbardeh, Marcos Zampieri, Cecilia Ovesdotter Alm, Travis Desell


Abstract
Event identification in technical logbooks poses challenges given the limited logbook data available in specific technical domains, the large set of possible classes, and logbook entries typically being in short form and non-standard technical language. Technical logbook data typically has both a domain, the field it comes from (e.g., automotive), and an application, what it is used for (e.g., maintenance). In order to better handle the problem of data scarcity, using a variety of technical logbook datasets, this paper investigates the benefits of using transfer learning from sources within the same domain (but different applications), from within the same application (but different domains) and from all available data. Results show that performing transfer learning within a domain provides statistically significant improvements, and in all cases but one the best performance. Interestingly, transfer learning from within the application or across the global dataset degrades results in all cases but one, which benefited from adding as much data as possible. A further analysis of the dataset similarities shows that the datasets with higher similarity scores performed better in transfer learning tasks, suggesting that this can be utilized to determine the effectiveness of adding a dataset in a transfer learning task for technical logbooks.
Anthology ID:
2022.lrec-1.450
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4235–4244
Language:
URL:
https://aclanthology.org/2022.lrec-1.450
DOI:
Bibkey:
Cite (ACL):
Farhad Akhbardeh, Marcos Zampieri, Cecilia Ovesdotter Alm, and Travis Desell. 2022. Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4235–4244, Marseille, France. European Language Resources Association.
Cite (Informal):
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets (Akhbardeh et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.450.pdf