Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Schick, Timo und Schütze, Hinrich (April 2021): Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. 16th Conference of the European Chapter of the Association for Computational Linguistics, Online, April 19-23, 2021. Merlo, Paola; Tiedemann, Jörg und Tsarfaty, Reut (Hrsg.): In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Stroudsburg, PA: Association for Computational Linguistics. S. 255-269

Abstract

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with “task descriptions” in natural language (e.g., Radford et al., 2019). While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin.

• Dokumententyp: Konferenzbeitrag (Paper)
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 400 Sprache > 400 Sprache ; 400 Sprache > 410 Linguistik
• Ort: Stroudsburg, PA
• Sprache: Englisch