Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation on one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning.
As supervised learning methods often require a lot of training data, active learning is a technique that selects a subset of data to annotate for training the best classifier. Existing active learning (AL) algorithms can be generally considered as three categories: 1) uncertainty sampling @cite_13 @cite_17 , which selects the data about which the current classifier is the most uncertain; 2) query by committee @cite_2 , which selects the data about which the committee'' disagree most; and 3) expected error reduction @cite_11 , which selects the data that can contribute the largest model loss reduction for the current classifier once labelled. Applications of active learning to NLP include text classification @cite_7 @cite_17 , relation classification @cite_14 , and structured prediction @cite_29 @cite_3 @cite_8 @cite_0 . Qian2014BilingualAL used uncertainty sampling to jointly perform on English and Chinese. stratos2015simple and zhang2016name deployed uncertainty-based AL algorithms for languages with the minimal supervision.
@cite_14 @cite_7 @cite_8 @cite_29 @cite_17 @cite_3 @cite_0 @cite_2 @cite_13 @cite_11
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Mining data with minimal annotation costs requires efficient active approaches, that ideally select the optimal candidate for labelling under a user-specified classification performance measure. Common generic approaches, that are usable with any classifier and any performance measure, are either slow like error reduction, or heuristics like uncertainty sampling. In contrast, our Probabilistic Active Learning (PAL) approach offers versatility, direct optimisation of a performance measure and computational efficiency. Given a labelling candidate from a pool, PAL models both the candidate’s label and the true posterior in its neighbourhood as random variables. By computing the expectation of the gain in classification performance over both random variables, PAL then selects the candidate that in expectation will improve the classification performance the most. Extending our recent poster, we discuss the properties of PAL and perform a thorough experimental evaluation on several synthetic and real-world data sets of different sizes. Results show comparable or better classification performance than error reduction and uncertainty sampling, yet PAL has the same asymptotic time complexity as uncertainty sampling and is faster than error reduction. Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier. AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data. We experimentally investigate the behavior of several AL strategies for sequence labeling tasks (in a partially-labeled scenario) tailored on Partially-Labeled Conditional Random Fields, on four sequence labeling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bioentity recognition. Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Medical Center's Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features)and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical decision theory for eliciting the relative costs of false positives and false negatives from the domain expert, constructing a utility measure of classification performance that integrates the expert preferences. Our findings suggest that the expert can, and should, provide more information than instance labels alone. In addition to achieving strong empirical results on the citation screening problem, this work outlines many important steps for moving away from simulated active learning and toward deploying AL for real-world applications. Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning (Cohn, Ghahramani, and Jordan 1996) serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data. Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. The learner first conveys its belief about an actively chosen image e.g. "I think this is a forest, what do you think?". If the learner is wrong, the supervisor provides an explanation e.g. "No, this is too open to be a forest". With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier. This rich human-machine communication leads to better classification performance. In this work, we propose three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label-but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories. Display Omitted Active learning is promising in the areas with complex topics in systematic reviews.Certainty criteria is promising to accelerate screening regardless of the topic.Certainty criteria performs as well as uncertainty criteria in classification.Weighting positive instances is promising to overcome the data imbalance.Unsupervised methods enhance the classification performance. In systematic reviews, the growing number of published studies imposes a significant screening workload on reviewers. Active learning is a promising approach to reduce the workload by automating some of the screening decisions, but it has been evaluated for a limited number of disciplines. The suitability of applying active learning to complex topics in disciplines such as social science has not been studied, and the selection of useful criteria and enhancements to address the data imbalance problem in systematic reviews remains an open problem. We applied active learning with two criteria (certainty and uncertainty) and several enhancements in both clinical medicine and social science (specifically, public health) areas, and compared the results in both. The results show that the certainty criterion is useful for finding relevant documents, and weighting positive instances is promising to overcome the data imbalance problem in both data sets. Latent dirichlet allocation (LDA) is also shown to be promising when little manually-assigned information is available. Active learning is effective in complex topics, although its efficiency is limited due to the difficulties in text classification. The most promising criterion and weighting method are the same regardless of the review topic, and unsupervised techniques like LDA have a possibility to boost the performance of active learning without manual annotation. In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to improve the global performance. Typically, these structured output scenarios are also characterized by a high cost associated with obtaining supervised training data, motivating the study of active learning for these situations. Starting with active learning approaches for multiclass classification, we first design querying functions for selecting entire structured instances, exploring the tradeoff between selecting instances based on a global margin or a combination of the margin of local classifiers. We then look at the setting where subcomponents of the structured instance can be queried independently and examine the benefit of incorporating structural information in such scenarios. Empirical results on both synthetic data and the semantic role labeling task demonstrate a significant reduction in the need for supervised training data when using the proposed methods. Active learning is a promising method to reduce human's effort for data annotation in different NLP applications. Since it is an iterative task, it should be stopped at some point which is optimum or near-optimum. In this paper we propose a novel stopping criterion for active learning of frame assignment based on the variability of the classifier's confidence score on the unlabeled data. The important advantage of this criterion is that we rely only on the unlabeled data to stop the data annotation process; as a result there are no requirements for the gold standard data and testing the classifier's performance in each iteration. Our experiments show that the proposed method achieves 93.67 of the classifier maximum performance. Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised learning algorithm that uses Gaussian fields and harmonic functions. This classifier is relational in nature: it relies on having the data presented as a partially labeled graph (also known as a within-network learning problem). This work showed yet again that empirical risk minimization (ERM) was the best method to find the next instance to label and provided an efficient way to compute ERM with the semi-supervised classifier. The computational problem with ERM is that it relies on computing the risk for all possible instances. If we could limit the candidates that should be investigated, then we can speed up active learning considerably. In the case where the data is graphical in nature, we can leverage the graph structure to rapidly identify instances that are likely to be good candidates for labeling. This paper describes a novel hybrid approach of using of community finding and social network analytic centrality measures to identify good candidates for labeling and then using ERM to find the best instance in this candidate set. We show on real-world data that we can limit the ERM computations to a fraction of instances with comparable performance. The recently proposed semi-supervised learning methods exploit consistency loss between different predictions under random perturbations. Typically, a student model is trained to predict consistently with the targets generated by a noisy teacher. However, they ignore the fact that not all training data provide meaningful and reliable information in terms of consistency. For misclassified data, blindly minimizing the consistency loss around them can hinder learning. In this paper, we propose a novel certainty-driven consistency loss (CCL) to dynamically select data samples that have relatively low uncertainty. Specifically, we measure the variance or entropy of multiple predictions under random augmentations and dropout as an estimation of uncertainty. Then, we introduce two approaches, i.e. Filtering CCL and Temperature CCL to guide the student learn more meaningful and certain reliable targets, and hence improve the quality of the gradients backpropagated to the student. Experiments demonstrate the advantages of the proposed method over the state-of-the-art semi-supervised deep learning methods on three benchmark datasets: SVHN, CIFAR10, and CIFAR100. Our method also shows robustness to noisy labels.