Modern NLP systems require high-quality annotated data. In specialized domains, expert annotations may be prohibitively expensive. An alternative is to rely on crowdsourcing to reduce costs at the risk of introducing noise. In this paper we demonstrate that directly modeling instance difficulty can be used to improve model performance, and to route instances to appropriate annotators. Our difficulty prediction model combines two learned representations: a universal' encoder trained on out-of-domain data, and a task-specific encoder. Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing. Our experiments confirm the expectation that for specialized tasks expert annotations are higher quality than crowd labels, and hence preferable to obtain if practical. Moreover, augmenting small amounts of expert data with a larger set of lay annotations leads to further improvements in model performance.
Crowdsourcing annotation is now a well-studied problem @cite_24 @cite_21 @cite_18 @cite_5 . Due to the noise inherent in such annotations, there have also been considerable efforts to develop aggregation models that minimize noise @cite_21 @cite_18 @cite_26 @cite_14 .
@cite_18 @cite_26 @cite_14 @cite_21 @cite_24 @cite_5
2040557557 2056584528 2130580359 2121324365
Crowdsourcing is the de-facto standard for gathering annotated data. While, in theory, data annotation tasks are assumed to be attempted by workers independently, in practice, data annotation tasks are often grouped into batches to be presented and annotated by workers together, in order to save on the time or cost overhead of providing instructions or necessary background. Thus, even though independence is usually assumed between annotations on data items within the same batch, in most cases, a worker's judgment on a data item can still be affected by other data items within the batch, leading to additional errors in collected labels. In this paper, we study the data annotation bias when data items are presented as batches to be judged by workers simultaneously. We propose a novel worker model to characterize the annotating behavior on data batches, and present how to train the worker model on annotation data sets. We also present a debiasing technique to remove the effect of such annotation bias from adversely affecting the accuracy of labels obtained. Our experimental results on synthetic and real-world data sets demonstrate that our proposed method can achieve up to +57 improvement in F1-score compared to the standard majority voting baseline. Crowdsourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their methods of motivating subjects to contribute and the scale of their applications. To date, there has yet to be a study that helps the practitioner to decide what form an annotation application should take to best reach its objectives within the constraints of a project. To fill this gap, we provide a faceted analysis of crowdsourcing from a practitioner's perspective, and show how our facets apply to existing published crowdsourced annotation applications. We then summarize how the major crowdsourcing genres fill different parts of this multi-dimensional space, which leads to our recommendations on the potential opportunities crowdsourcing offers to future annotation efforts. Crowdsourcing is a promising way to reduce the effort of collecting annotations for training gesture recognition systems. Crowdsourced annotations suffer from "noise" such as mislabeling, or inaccurate identification of start and end time of gesture instances. In this paper we present SegmentedLCSS and WarpingLCSS, two template-matching methods offering robustness when trained with noisy crowdsourced annotations to spot gestures from wearable motion sensors. The methods quantize signals into strings of characters and then apply variations of the longest common subsequence algorithm (LCSS) to spot gestures. We compare the noise robustness of our methods against baselines which use dynamic time warping (DTW) and support vector machines (SVM). The experiments are performed on data sets with various gesture classes (10-17 classes) recorded from accelerometers on arms, with both real and synthetic crowdsourced annotations. WarpingLCSS has similar or better performance than baselines in absence of noisy annotations. In presence of 60 mislabeled instances, WarpingLCSS outperformed SVM by 22 F1-score and outperformed DTW-based methods by 36 F1-score on average. SegmentedLCSS yields similar performance as WarpingLCSS, however it performs one order of magnitude slower. Additionally, we show to use our methods to filter out the noise in the crowdsourced annotation before training a traditional classifier. The filtering increases the performance of SVM by 20 F1-score and of DTW-based methods by 8 F1-score on average in the noisy real crowdsourced annotations. Crowdsourcing utilizes human ability by distributing tasks to a large number of workers. It is especially suitable for solving data clustering problems because it provides a way to obtain a similarity measure between objects based on manual annotations, which capture the human perception of similarity among objects. This is in contrast to most clustering algorithms that face the challenge of finding an appropriate similarity measure for the given dataset. Several algorithms have been developed for crowdclustering that combine partial clustering results, each obtained by annotations provided by a different worker, into a single data partition. However, existing crowdclustering approaches require a large number of annotations, due to the noisy nature of human annotations, leading to a high computational cost in addition to the large cost associated with annotation. We address this problem by developing a novel approach for crowclustering that exploits the technique of matrix completion. Instead of using all the annotations, the proposed algorithm constructs a partially observed similarity matrix based on a subset of pairwise annotation labels that are agreed upon by most annotators. It then deploys the matrix completion algorithm to complete the similarity matrix and obtains the final data partition by applying a spectral clustering algorithm to the completed similarity matrix. We show, both theoretically and empirically, that the proposed approach needs only a small number of manual annotations to obtain an accurate data partition. In effect, we highlight the trade-off between a large number of noisy crowdsourced labels and a small number of high quality labels.