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 …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Large transformer-based language models (LMs) trained on huge text corpora have shown
unparalleled generation capabilities. However, controlling attributes of the generated …

Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead,
we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

For machine translation, a vast majority of language pairs in the world are considered low-
resource because they have little parallel data available. Besides the technical challenges …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Advanced neural language models (NLMs) are widely used in sequence generation tasks
because they are able to produce fluent and meaningful sentences. They can also be used …

The external language models (LM) integration remains a challenging task for end-to-end
(E2E) automatic speech recognition (ASR) which has no clear division between acoustic …

Aligning language models with human preferences is crucial for reducing errors and biases
in these models. Alignment techniques, such as reinforcement learning from human …