A General-Purpose Crowdsourcing Computational Quality Control Toolkit for Python

09/17/2021 ∙ by Dmitry Ustalov, et al. ∙ Moscow Institute of Physics and Technology Yandex 0

Quality control is a crux of crowdsourcing. While most means for quality control are organizational and imply worker selection, golden tasks, and post-acceptance, computational quality control techniques allow parameterizing the whole crowdsourcing process of workers, tasks, and labels, inferring and revealing relationships between them. In this paper, we demonstrate Crowd-Kit, a general-purpose crowdsourcing computational quality control toolkit. It provides efficient implementations in Python of computational quality control algorithms for crowdsourcing, including uncertainty measures and crowd consensus methods. We focus on aggregation methods for all the major annotation tasks, from the categorical annotation in which latent label assumption is met to more complex tasks like image and sequence aggregation. We perform an extensive evaluation of our toolkit on several datasets of different nature, enabling benchmarking computational quality control methods in a uniform, systematic, and reproducible way using the same codebase. We release our code and data under an open-source license at https://github.com/Toloka/crowd-kit.

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Introduction

Means for quality control in crowdsourcing include organizational approaches, such as task design, decomposition, golden tasks preparation, yet reliably automated, and computational approaches that employ relationships and statistical properties of workers, tasks, and labels. Many crowdsourcing studies of complex crowdsourcing pipelines aim to reduce their tasks to multi-classification or combine multi-classification with post-acceptance, e.g., in a seminal paper by Bernstein et al. (2010)

. At the same time, researchers from such fields as natural language processing, computer vision, and others develop discipline-specific methods. To be conveniently employed, these methods need to be integrated with popular data science libraries and frameworks. However, such toolkits as SQUARE

(Sheshadri and Lease, 2013), CEKA (Zhang et al., 2015), Truth Inference (Zheng et al., 2017), spark-crowd (Rodrigo et al., 2019), require additional effort to be embedded in applications. We believe in addressing this issue by developing Crowd-Kit, an open-source production-ready Python toolkit for computational quality control in crowdsourcing. It implements popular quality control methods, providing a common ground for reliable experimentation and application. We perform an extensive evaluation of the Crowd-Kit library to provide the common ground for comparisons. In all the experiments in this paper, we used our implementations of the corresponding methods.

Crowd-Kit Design and Maintenance

Our fundamental design principle of Crowd-Kit is to bridge the gap between crowd science and vivid data science ecosystem of NumPy, SciPy, Pandas, and scikit-learn (Pedregosa et al., 2011). We implemented Crowd-Kit in Python and employ the highly optimized data structures and algorithms available in these libraries, ensuring compatibility with the application programming interface (API) of scikit-learn and data frames of Pandas.

We implemented all the methods in Crowd-Kit from scratch in Python. Although unlike spark-crowd (Rodrigo et al., 2019) our library does not provide means for running on a distributed computational cluster, it leverages efficient implementations of numerical algorithms in underlying libraries widely used in the research community. Besides aggregation methods, Crowd-Kit offers annotation quality characteristics, such as uncertainty (Malinin, 2019) and agreement with aggregate (Appen Limited, 2021).

Crowd-Kit is platform-agnostic, allowing analyzing data from any crowdsourcing marketplace (as soon as one can download the data). Crowd-Kit is an open-source library available under Apache license both on GitHub and PyPI: https://github.com/Toloka/crowd-kit and https://pypi.org/project/crowd-kit/, correspondingly.

Categorical Aggregation

Crowd-Kit includes aggregation methods for categorical data, in which latent label assumption is met. We implement most traditional methods for categorical answer aggregation, including such models as Dawid-Skene (DS, 1979), GLAD (Whitehill et al., 2009), and M-MSR (Ma and Olshevsky, 2020). We also offer an implementation of Majority Vote (MV) as well as its such weighted variations as Worker Agreement with Aggregate (Wawa) as described in Appen Limited (2021).

Method D_Product D_PosSent S_Rel S_Adult binary1 binary2
MV
Wawa
DS
GLAD
M-MSR
Table 1: Comparison of the implemented categorical aggregation methods (accuracy is used).

Evaluation.

To ensure the correctness of our implementations, we compared the observed aggregation quality with the already available implementations by Zheng et al. (2017) and Rodrigo et al. (2019) on the same datasets. Table 1 shows evaluation results, indicating a similar level of quality as them.

Pairwise Aggregation

Pairwise comparisons are essential for such tasks as information retrieval evaluation and subjective opinion gathering, where the latent label assumption is not met. We implemented the Bradley-Terry probabilistic transitivity model (BT, 1952) for pairwise comparisons.

Evaluation.

Table 2 shows the comparison of the Bradley-Terry method implemented in Crowd-Kit to the random baseline on the graded readability dataset by Chen et al. (2013)

. Since it contains only 491 items, we additionally annotated on Toloka a sample of 2,497 images from the IMDB-WIKI dataset

(Rothe et al., 2018). This dataset contains images of people with reliable ground-truth age assigned to every image. The annotation allowed us to obtain 84,543 comparisons by 2,085 workers.

Method Chen et al. (2013) IMDB-WIKI
Bradley-Terry
Random
Table 2: Comparison of implemented pairwise aggregation methods (NDCG@10 is used for Chen et al. (2013) and NDCG@100 is used for IMDB-WIKI).

Sequence Aggregation

Crowd-Kit implements the Recognizer Output Voting Error Reduction (ROVER) dynamic programming algorithm by Fiscus (1997), known for its successful application in crowdsourced sequence aggregation (Marge et al., 2010). We also offer implementations of Reliability Aware Sequence Aggregation (RASA and HRRASA) algorithms by Li and Fukumoto (2019) and Li (2020)

that encode responses using Transformer-based representations and then iteratively estimate the aggregated response embedding.

Evaluation.

We used two datasets, CrowdWSA (Li and Fukumoto, 2019) and CrowdSpeech (Pavlichenko et al., 2021). As the typical application for sequence aggregation in crowdsourcing is audio transcription, we used the word error rate as the quality criterion (Fiscus, 1997) in Table 3.

Dataset Version ROVER RASA HRRASA
CrowdWSA J1
T1
T2
CrowdSpeech dev-clean
dev-other
test-clean
test-other
Table 3: Comparison of implemented sequence aggregation methods (average word error rate is used).

Image Aggregation

Crowd-Kit offers three image segmentation aggregation methods. First, it provides a trivial pixel-wise MV. Second, it implements a method similar to the one described in Jung-Lin Lee et al. (2018)

, performing an EM algorithm for counting the probability of a correct answer as the proportion of correctly classified pixels to the number of all pixels that at least one worker chose. Third, we implement a variation of RASA that scores Jaccard distances between segments and weights them proportionally to these distances.

Evaluation.

We annotated on Toloka a sample of 2,000 images from the MS COCO

(Lin et al., 2014) dataset consisting of four object labels. For each image, nine workers submitted segmentations. In total, we received 18,000 responses. Table 4 shows the comparison of the methods on the above-described dataset using the intersection over union (IoU) criterion.

Dataset MV EM RASA
MS COCO
Table 4: Comparison of implemented image aggregation algorithms (IoU is used).

Conclusion

Our experience in running Crowd-Kit for processing crowdsourced data shows that it successfully handles industry-scale datasets without the need for a large computational cluster. We currently focus on providing a consistent API for benchmarking existing methods and implementing additional domain-specific aggregation techniques like sequence labels aggregation (Nguyen et al., 2017) and continuous answer aggregation. We believe that the availability of computational quality control techniques in a standardized way would open new venues for reliable improvement of the crowdsourcing quality beyond the traditional well-known methods and pipelines.

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