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 …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Active learning (AL) is an effective sample selection approach that annotates only a subset
of the training data to address the challenge of data annotation, and deep learning (DL) is …

Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects
informative unlabeled data for human annotation, aiming to improve over random sampling …

Iterative preference learning, though yielding superior performances, requires online
annotated preference labels. In this work, we study strategies to select worth-annotating …

In recent years, active learning has been successfully applied to an array of NLP tasks.
However, prior work often assumes that training and test data are drawn from the same …

The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled
data due to the extremely expensive and time-consuming process involved in manual …

Spoken language is fundamentally different from the written language in that it contains
frequent disfluencies or parts of an utterance that are corrected by the speaker. Disfluency …

Though Large Language Models (LLMs) have demonstrated the powerful capabilities of few-
shot learning through prompting methods, supervised training is still necessary for complex …

We consider the task of optimally fine-tuning pre-trained multilingual models, given small
amounts of unlabelled target data and an annotation budget. In this paper, we introduce …