Abstract
Humor is a magnetic component in everyday human interactions and communications. Computationally modeling humor enables NLP systems to entertain and engage with users. We investigate the effectiveness of prompting, a new transfer learning paradigm for NLP, for humor recognition. We show that prompting performs similarly to finetuning when numerous annotations are available, but gives stellar performance in low-resource humor recognition. The relationship between humor and offense is also inspected by applying influence functions to prompting; we show that models could rely on offense to determine humor during transfer.
Dokumententyp: | Konferenzbeitrag (Paper) |
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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: | 400 Sprache > 400 Sprache
400 Sprache > 410 Linguistik |
URN: | urn:nbn:de:bvb:19-epub-107443-9 |
Sprache: | Englisch |
Dokumenten ID: | 107443 |
Datum der Veröffentlichung auf Open Access LMU: | 20. Okt. 2023, 08:17 |
Letzte Änderungen: | 20. Okt. 2023, 08:17 |