Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Pretrained language models (PLMs) are trained on massive corpora, but often need to
specialize to specific domains. A parameter-efficient adaptation method suggests training an …

We introduce Adapters, an open-source library that unifies parameter-efficient and modular
transfer learning in large language models. By integrating 10 diverse adapter methods into a …

Multilingual sequence-to-sequence models perform poorly with increased language
coverage and fail to consistently generate text in the correct target language in few-shot …

Code-mixing and code-switching are frequent features in online conversations.
Classification of such text is challenging if one of the languages is low-resourced. Fine …

State-of-the-art language models in NLP perform best when fine-tuned even on small
datasets, but due to their increasing size, fine-tuning and downstream usage have become …

Sinhala is the native language of the Sinhalese people who make up the largest ethnic
group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo …

Commonsense reasoning systems should be able to generalize to diverse reasoning cases.
However, most state-of-the-art approaches depend on expensive data annotations and …

Generalising to unseen domains is under-explored and remains a challenge in neural
machine translation. Inspired by recent research in parameter-efficient transfer learning from …

Prompt tuning approaches, which learn task-specific soft prompts for a downstream task
conditioning on frozen pre-trained models, have attracted growing interest due to its …