A Survey of Active Learning for Natural Language Processing (2022)

Abstract: In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.

## introduction the majority of modern natural language processing (nlp) systems are based on data-driven machine learning models. the success of these models depends on the quality and quantity of the available target training data. while these models can obtain impressive performance if given enough supervision, it is usually expensive to collect large amounts of annotations, especially considering that the labeling process can be laborious and challenging for nlp tasks ( §3.2). active learning (al), an approach that aims to achieve high accuracy with fewer training labels by allowing a model to choose the data to be annotated and used for learning, is a widely-studied approach to tackle this labeling bottleneck @xcite . active learning has been studied for more than twenty years @xcite @xcite and there have been several literature surveys on this topic @xcite @xcite @xcite @xcite . nevertheless, there is still a lack of an al survey for nlp that includes recent advances. @xcite and @xcite provide great surveys covering al for nlp, but these surveys are now more than a decade old. in the meantime, the field of nlp has been transformed by deep learning. while other more recent surveys cover deep active learning, they are either too specific, focused only on text classification @xcite , or too general, covering ai applications more broadly @xcite . moreover, applying al to nlp tasks requires specific considerations, e.g. handling complex output structures and trade-offs in text annotation cost ( §3), which have not been thoroughly discussed. in order to provide an nlp-specific al survey, foot_0 we start by searching the acl anthology for alrelated papers. we simply search for the keyword "active" in paper titles and then perform manual filtering. we also gradually include relevant papers missed by keyword search and papers from other venues encountered by following reference links throughout the surveying process. 2 the distribution of al-related papers in the acl anthology over the past twenty years is shown in figure 1 , which also includes rough counts of works concerning neural models by searching for the word "neural" in titles. the overall trend is interesting. there is a peak around the years of 2009 and 2010, while the counts drop and fluctuate during the mid-2010s, which corresponds to the time when neural models became prominent in nlp. we observe a renewed interest in al research in recent years, which is algorithm 1 a typical active learning procedure. input: an unlabeled data pool u. output: the final labeled dataset l and trained model m. 1: l, u ← seed(u) ▷ start ( §5.1) 2: m ← train(l, u) ▷ model learning ( §4) 3: while not stop_criterion() do ▷ stop ( §5.2) 4: i ← query(m, u) ▷ query ( §2, §3) 5: i ′ ← annotate(i) ▷ annotate ( §3) 6: u ← u -i; l ← l ∪ i ′ 7: m ← train(l, u) ▷ model learning ( §4) 8: return l, m f primarily focused on deep active learning @xcite .