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TU Berlin

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Neslihan Iskender

Lupe

Research Group

Crowdsourcing and Open Data

 

Teaching

  • Study Project Quality & Usability (Since SS 2018)
  • Interdiziplinäres Medienprojekt (Since SS 2018)
  • Usability Engineering (Exercise SS 2018)

 

Biography

Neslihan Iskender received her Bachelor and Master of Science degree in Industrial Engineering and Management at the Karlsruhe Institute of Technology. During her studies, she focused on managing new technologies and innovation management. Since May 2017, she is employed as a research assistant at the Quality and Usability Labs where she is working towards a PhD in the field of crowdsourcing. Her research Topics are:

  • Crowd assessments: Usability, UX, QoE, Quality
  • Real-time interaction, human computation as a service, (HuaaS)
  • Hybrid Worfklows for micro-task crowdsourcing
  • Internal Crowdsourcing

 

Current Projects

 

Past Projects

 

Contact

E-Mail: neslihan.iskender@tu-berlin.de

Phone: +49 (30) 8353-58347 

Fax: +49 (30) 8353-58409 

 

Address

Quality and Usability Lab

Deutsche Telekom Laboratories

Technische Universität Berlin

Ernst-Reuter-Platz 7

D-10587 Berlin, Germany 

 

 

Publications

Reliability of Human Evaluation for Text Summarization: Lessons Learned and Challenges Ahead
Citation key iskender2021b
Author Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian
Title of Book Proceedings of the Workshop on Human Evaluation of NLP Systems
Pages 86–96
Year 2021
ISBN 978-1-954085-10-7
Location online
Address online
Month apr
Note online
Publisher Association for Computational Linguistics
Series HumEval
How Published Fullpaper
Abstract Only a small portion of research papers with human evaluation for text summarization provide information about the participant demographics, task design, and experiment protocol. Additionally, many researchers use human evaluation as gold standard without questioning the reliability or investigating the factors that might affect the reliability of the human evaluation. As a result, there is a lack of best practices for reliable human summarization evaluation grounded by empirical evidence. To investigate human evaluation reliability, we conduct a series of human evaluation experiments, provide an overview of participant demographics, task design, experimental set-up and compare the results from different experiments. Based on our empirical analysis, we provide guidelines to ensure the reliability of expert and non-expert evaluations, and we determine the factors that might affect the reliability of the human evaluation.
Link to publication Link to original publication Download Bibtex entry

Publications

2018

Barz, Michael and Büyükdemircioglu, Neslihan and Prasad Surya, Rikhu and Polzehl, Tim and Sonntag, Daniel (2018). Device-Type Influence in Crowd-based Natural Language Translation Tasks. Proceedings of the 1st Workshop on Subjectivity, Ambiguity and Disagreement (SAD) in Crowdsourcing 2018, and the 1st Workshop CrowdBias'18: Disentangling the Relation Between Crowdsourcing and Bias Management, 93–97.

Link to publication Link to original publication

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