In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

The notion of" in-domain data" in NLP is often over-simplistic and vague, as textual data
varies in many nuanced linguistic aspects such as topic, style or level of formality. In …

Current state-of-the-art NMT systems use large neural networks that are not only slow to
train, but also often require many heuristics and optimization tricks, such as specialized …

Bangla--ranked as the 6th most widely spoken language across the world (https://www.
ethnologue. com/guides/ethnologue200), with 230 million native speakers--is still …

Corpus based approaches to automatic translation such as Example Based and Statistical
Machine Translation systems use large amounts of parallel data created by humans to train …

Despite being the seventh most widely spoken language in the world, Bengali has received
much less attention in machine translation literature due to being low in resources. Most …

We introduce submodular optimization to the problem of training data subset selection for
statistical machine translation (SMT). By explicitly formulating data selection as a …

Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is
efficient as it does not require any parameter updates to the trained LLM, but only few …

Construction of human-curated annotated datasets for abstractive text summarization (ATS)
is very time-consuming and expensive because creating each instance requires a human …

We study the application of active learning techniques to the translation of unbounded data
streams via interactive neural machine translation. The main idea is to select, from an …