5 papers · 1 filter
Rashid: A Cipher-Based Framework for Exploring In-Context Language Learning
Niyati Bafna, Ryan Soh-Eun Shim, Barbara Plank +2
Where there is growing interest in in-context language learning (ICLL) for unseen languages with large language models, such languages usually suffer from the lack of NLP tools, da…
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models
Niyati Bafna, Emily Chang, Nathaniel R. Robinson +4
Most of the world's languages and dialects are low-resource, and lack support in mainstream machine translation (MT) models. However, many of them have a closely-related high-resou…
Paradigm Completion for Derivational Morphology
Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah +2
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the the…
Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization
Niyati Bafna, Kenton Murray, David Yarowsky
While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and…
Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!
Niyati Bafna, Philipp Koehn, David Yarowsky
While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. I…