Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-30386Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Helland, Solveig | - |
| dc.contributor.author | Gavagnin, Elena | - |
| dc.contributor.author | de Spindler, Alexandre | - |
| dc.date.accessioned | 2024-03-27T12:40:43Z | - |
| dc.date.available | 2024-03-27T12:40:43Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://aclanthology.org/2023.swisstext-1.7 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30386 | - |
| dc.description.abstract | The growing capabilities of transformer models pave the way for solving increasingly complex NLP tasks. A key to supporting applicationspecific requirements is the ability to fine-tune. However, compiling a fine-tuning dataset tailored to complex tasks is tedious and results in large datasets, limiting the ability to control transformer output. We present an approach in which complex tasks are divided into simpler subtasks. Multiple transformer models are fine-tuned to one subtask each, and lined up to accomplish the complex task. This simplifies the compilation of fine-tuning datasets and increases overall controllability. Using the example of reducing gender bias as a complex task, we demonstrate our approach and show that it performs better than using a single model. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Association for Computational Linguistics | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject.ddc | 410.285: Computerlinguistik | de_CH |
| dc.title | Divide et impera : multi-transformer architectures for complex NLP-tasks | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Management and Law | de_CH |
| zhaw.organisationalunit | Institut für Wirtschaftsinformatik (IWI) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-30386 | - |
| zhaw.conference.details | 8th Swiss Text Analytics Conference – SwissText 2023, Neuchâtel, Switzerland, 12-14 June 2023 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 75 | de_CH |
| zhaw.pages.start | 70 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | Proceedings of the 8th edition of the Swiss Text Analytics Conference | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Management and Law | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Helland-etal_Multi-transformer-architectures-for-complex-NLP-tasks.pdf | 157.83 kB | Adobe PDF |  View/Open |
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Helland, S., Gavagnin, E., & de Spindler, A. (2023). Divide et impera : multi-transformer architectures for complex NLP-tasks [Conference paper]. Proceedings of the 8th Edition of the Swiss Text Analytics Conference, 70–75. https://doi.org/10.21256/zhaw-30386
Helland, S., Gavagnin, E. and de Spindler, A. (2023) ‘Divide et impera : multi-transformer architectures for complex NLP-tasks’, in Proceedings of the 8th edition of the Swiss Text Analytics Conference. Association for Computational Linguistics, pp. 70–75. Available at: https://doi.org/10.21256/zhaw-30386.
S. Helland, E. Gavagnin, and A. de Spindler, “Divide et impera : multi-transformer architectures for complex NLP-tasks,” in Proceedings of the 8th edition of the Swiss Text Analytics Conference, 2023, pp. 70–75. doi: 10.21256/zhaw-30386.
HELLAND, Solveig, Elena GAVAGNIN und Alexandre DE SPINDLER, 2023. Divide et impera : multi-transformer architectures for complex NLP-tasks. In: Proceedings of the 8th edition of the Swiss Text Analytics Conference [online]. Conference paper. Association for Computational Linguistics. 2023. S. 70–75. Verfügbar unter: https://aclanthology.org/2023.swisstext-1.7
Helland, Solveig, Elena Gavagnin, and Alexandre de Spindler. 2023. “Divide et Impera : Multi-Transformer Architectures for Complex NLP-Tasks.” Conference paper. In Proceedings of the 8th Edition of the Swiss Text Analytics Conference, 70–75. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-30386.
Helland, Solveig, et al. “Divide et Impera : Multi-Transformer Architectures for Complex NLP-Tasks.” Proceedings of the 8th Edition of the Swiss Text Analytics Conference, Association for Computational Linguistics, 2023, pp. 70–75, https://doi.org/10.21256/zhaw-30386.
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