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A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification
Machine Learning · Submitted 23 Sep 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies.
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The abstract, sentence by sentence
Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s1 | For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. | background 0.97 | 0.06 | 0.05 | asserted 0.99 |
| s2 | Traditional AL strategies operate in an idealized framework. | background 0.95 | 0.05 | 0.02 | asserted 1.00 |
| s3 | They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. | background 0.95 | 0.05 | 0.02 | asserted 1.00 |
| s4 | However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. | background 0.84 | 0.06 | 0.04 | asserted 0.76 |
| s5 | Recently, a wide range of novel AL strategies has been proposed to address these issues. | background 0.96 | 0.05 | 0.03 | asserted 1.00 |
| s6 | They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. | background 0.84 | 0.07 | 0.07 | asserted 0.99 |
| s7 | (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. | background 0.73 | 0.08 | 0.03 | asserted 1.00 |
| s8 | (3) They take more complex cost schemes regarding annotations and misclassifications into account. | background 0.77 | 0.07 | 0.05 | asserted 1.00 |
| s9 | This survey provides an overview of these AL strategies and refers to them as real-world AL. | contribution 0.98 | 0.61 | 0.11 | asserted 0.97 |
| s10 | Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. | contribution 0.74 | 0.94 | 0.17 | asserted 1.00 |
| s11 | Finally, we outline possible directions for future research in the field of AL. | future work 1.00 | 0.80 | 0.09 | asserted 0.38 |
What was examined, and what was not
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The paper
Original abstract
Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
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What it cites · 4 sentences without a confident label
Cites as support 3 papers
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Furthermore, in the setting, its estimation based on a separate set of instances is challenging because of a sampling bias (arising from the active data acquisition) [ref] and the lack of known ground truth class labels (arising from the error-proneness of the annotators).
Limitations of Assessing Active Learning Performance at Runtime · Misclassification Cost · 0.98
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However, a query may not only improve the classification model but additionally the queried annotator [ref] .
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience · Active Learning for Classification · 0.97
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Instead, the number of objects within an image is more appropriate [ref] because annotating images with many objects is more time-intensive.
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation · Active Learning Issues Beyond Classification · 0.99
Cites as background 1 paper
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Other commonly used terms are oracle [ref] , expert [ref] , worker [ref] , teacher [ref] , and labeler [ref] .
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data · Introduction · 0.88
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