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http://dx.doi.org/10.18419/opus-14247
Autor(en): | Kann, Katharina Ebrahimi, Abteen Mager, Manuel Oncevay, Arturo Ortega, John E. Rios, Annette Fan, Angela Gutierrez-Vasques, Ximena Chiruzzo, Luis Giménez-Lugo, Gustavo A. Ramos, Ricardo Meza Ruiz, Ivan Vladimir Mager, Elisabeth Chaudhary, Vishrav Neubig, Graham Palmer, Alexis Coto-Solano, Rolando Vu, Ngoc Thang |
Titel: | AmericasNLI : machine translation and natural language inference systems for Indigenous languages of the Americas |
Erscheinungsdatum: | 2022 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 17 |
Erschienen in: | Frontiers in artificial intelligence 5 (2022), No. 995667 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-142668 http://elib.uni-stuttgart.de/handle/11682/14266 http://dx.doi.org/10.18419/opus-14247 |
ISSN: | 2624-8212 |
Zusammenfassung: | Little attention has been paid to the development of human language technology for truly low-resource languages - i.e., languages with limited amounts of digitally available text data, such as Indigenous languages. However, it has been shown that pretrained multilingual models are able to perform crosslingual transfer in a zero-shot setting even for low-resource languages which are unseen during pretraining. Yet, prior work evaluating performance on unseen languages has largely been limited to shallow token-level tasks. It remains unclear if zero-shot learning of deeper semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, a natural language inference dataset covering 10 Indigenous languages of the Americas. We conduct experiments with pretrained models, exploring zero-shot learning in combination with model adaptation. Furthermore, as AmericasNLI is a multiway parallel dataset, we use it to benchmark the performance of different machine translation models for those languages. Finally, using a standard transformer model, we explore translation-based approaches for natural language inference. We find that the zero-shot performance of pretrained models without adaptation is poor for all languages in AmericasNLI, but model adaptation via continued pretraining results in improvements. All machine translation models are rather weak, but, surprisingly, translation-based approaches to natural language inference outperform all other models on that task. |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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frai-05-995667.pdf | 850,61 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons