We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …

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 …

Active learning seeks to achieve strong performance with fewer training samples. It does this
by iteratively asking an oracle to label newly selected samples in a human-in-the-loop …

Large-scale deployment of generative AI tools often depends on costly API calls to a Large
Language Model (LLM) to fulfil user queries. To curtail the frequency of these calls, one can …

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 …

Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this
problem in an active learning setting where we can spend a given budget on translating in …

Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.
However, collecting a large amount of turn-by-turn annotated dialogue data is costly and …

Active Learning (AL) is a method to iteratively select data for annotation from a pool of
unlabeled data, aiming to achieve better model performance than random selection …

The machine translation mechanism translates texts automatically between different natural
languages, and Neural Machine Translation (NMT) has gained attention for its rational …

Neural machine translation (MT) systems have been shown to perform poorly on low-
resource language pairs, for which large-scale parallel data is unavailable. Making the data …