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            <namePart type="given">Su</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Towards Hybrid Human-Machine Workflow for Natural Language Generation
%A Iskender, Neslihan
%A Polzehl, Tim
%A Möller, Sebastian
%Y Blodgett, Su Lin
%Y Madaio, Michael
%Y O’Connor, Brendan
%Y Wallach, Hanna
%Y Yang, Qian
%S Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F iskender-etal-2021-towards
%X 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.
%U https://aclanthology.org/2021.hcinlp-1.1/
%P 1-7

Markdown (Informal)

[Towards Hybrid Human-Machine Workflow for Natural Language Generation](https://aclanthology.org/2021.hcinlp-1.1/) (Iskender et al., HCINLP 2021)

• Towards Hybrid Human-Machine Workflow for Natural Language Generation (Iskender et al., HCINLP 2021)

ACL

• Neslihan Iskender, Tim Polzehl, and Sebastian Möller. 2021. Towards Hybrid Human-Machine Workflow for Natural Language Generation. In Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing, pages 1–7, Online. Association for Computational Linguistics.