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TU Berlin

Inhalt des Dokuments

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

Towards Hybrid Human-Machine Workflow for Natural Language Generation
Zitatschlüssel iskender2021c
Autor Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian
Buchtitel Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
Seiten 1–7
Jahr 2021
ISBN 978-1-954085-17-6
Ort online
Adresse online
Monat apr
Notiz online
Verlag Association for Computational Linguistics
Serie HCI + NLP
Wie herausgegeben Fullpaper
Zusammenfassung In recent years, crowdsourcing has gained much attention from researchers to generate data for the Natural Language Generation (NLG) tools or to evaluate them. However, the quality of crowdsourced data has been questioned repeatedly because of the complexity of NLG tasks and crowd workers' unknown skills. Moreover, crowdsourcing can also be costly and often not feasible for large-scale data generation or evaluation. To overcome these challenges and leverage the complementary strengths of humans and machine tools, we propose a hybrid human-machine workflow designed explicitly for NLG tasks with real-time quality control mechanisms under budget constraints. This hybrid methodology is a powerful tool for achieving high-quality data while preserving efficiency. By combining human and machine intelligence, the proposed workflow decides dynamically on the next step based on the data from previous steps and given constraints. Our goal is to provide not only the theoretical foundations of the hybrid workflow but also to provide its implementation as open-source in future work.
Link zur Publikation Link zur Originalpublikation Download Bibtex Eintrag

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 zur Publikation Link zur Originalpublikation

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