Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

We present a simple yet effective approach to build multilingual speech-to-text (ST)
translation by efficient transfer learning from pretrained speech encoder and text decoder …

Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP.
Adapter tuning consists in freezing pretrained parameters of a model and injecting …

The remarkable success of large language models has been driven by dense models
trained on massive unlabeled, unstructured corpora. These corpora typically contain text …

We consider the problem of multilingual unsupervised machine translation, translating to
and from languages that only have monolingual data by using auxiliary parallel language …

Prompting has recently been shown as a promising approach for applying pre-trained
language models to perform downstream tasks. We present Multi-Stage Prompting (MSP), a …

Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a
multilingual pretrained encoder (MPE), or improving the performance on supervised …

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the
performance of large language models (LLMs) on the downstream task. However, there are …