• Annotator rationales for labeling tasks in crowdsourcing 

      Kutlu, Mucahid; McDonnell, Tyler; Elsayed, Tamer; Lease, Matthew ( Elsevier , 2020 , Article)
      When collecting item ratings from human judges, it can be difficult to measure and enforce data quality due to task subjectivity and lack of transparency into how judges make each rating decision. To address this, we ...
    • Efficient Test Collection Construction via Active Learning 

      Rahman, Md Mustafizur; Kutlu, Mucahid; Elsayed, Tamer; Lease, Matthew ( Association for Computing Machinery , 2020 , Conference Paper)
      To create a new IR test collection at low cost, it is valuable to carefully select which documents merit human relevance judgments. Shared task campaigns such as NIST TREC pool document rankings from many participating ...
    • The many benefits of annotator rationales for relevance judgments 

      McDonnell, Tyler; Kutlu, Mucahid; Elsayed, Tamer; Lease, Matthew ( International Joint Conferences on Artificial Intelligence , 2017 , Conference Paper)
      When collecting subjective human ratings of items, it can be difficult to measure and enforce data quality due to task subjectivity and lack of insight into how judges arrive at each rating decision. To address this, we ...
    • Mix and match: Collaborative expert-crowd judging for building test collections accurately and affordably 

      Kutlu, Mucahid; McDonnell, Tyler; Sheshadri, Aashish; Elsayed, Tamer; Lease, Matthew ( CEUR-WS , 2018 , Conference Paper)
      Crowdsourcing offers an affordable and scalable means to collect relevance judgments for information retrieval test collections. However, crowd assessors may showhigher variance in judgment quality than trusted assessors. ...
    • Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to Ensure Quality Relevance Annotations 

      Goyal, Tanya; McDonnell, Tyler; Kutlu, Mucahid; Elsayed, Tamer; Lease, Matthew ( AAAI Press , 2018 , Conference Paper)
      While peer-agreement and gold checks are well-established methods for ensuring quality in crowdsourced data collection, we explore a relatively new direction for quality control: estimating work quality directly from ...