HumanAL: Calibrating Human Matching Beyond a Single Task
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Abstract (arXiv)
This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a behavioral profile for human annotators which is used as a feature representation of the provided input. We show that by utilizing black-box machine learning, we can take into account human behavior and calibrate their input to improve the labeling quality. To support our claims and provide a proof-of-concept, we experiment with three different matching tasks, namely, schema matching, entity matching and text matching. Our empirical evaluation suggests that the method can improve the quality of gathered labels in multiple settings including cross-domain (across different matching tasks).
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 59 references that are in the map. First posted on arXiv 6 May 2022.
Builds on
18 of 59 references.
References · 18
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
- 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
- 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
- 2016Text Matching as Image Recognitioncited by 59
- 2020FIND: Human-in-the-Loop Debugging Deep Text Classifierscited by 40
- 2019Low-resource Deep Entity Resolution with Transfer and Active Learningcited by 37
- 2019Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmarkcited by 32
- 2019Multi-Level Matching and Aggregation Network for Few-Shot Relation Classificationcited by 32
- 2019ZeroER: Entity Resolution using Zero Labeled Examplescited by 28
- 2019MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matchingcited by 17
- 2020A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matchingcited by 17
- 2018Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actionscited by 16
6 more
- 2021Deep Indexed Active Learning for Matching Heterogeneous Entity Representationscited by 9
- 2021Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptationcited by 7
- 2018Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Ratescited by 6
- 2020Learning to Characterize Matching Expertscited by 5
- 2021PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matchingcited by 4
- 2016Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Managementcited by 2
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.