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- Publisher Website: 10.24963/ijcai.2019/193
- Scopus: eid_2-s2.0-85074946853
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Article: MiSC: Mixed Strategies Crowdsourcing
Title | MiSC: Mixed Strategies Crowdsourcing |
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Authors | |
Keywords | Humans and AI: Human Computation and Crowdsourcing Humans and AI: Human-AI Collaboration |
Issue Date | 2019 |
Publisher | International Joint Conferences on Artificial Intelligence. The Proceedings' website is located at https://www.ijcai.org/proceedings/2019/ |
Citation | Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 10-16 August 2019, p. 1394-1400 How to Cite? |
Abstract | Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose MiSC (Mixed Strategies Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments. |
Persistent Identifier | http://hdl.handle.net/10722/275281 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Ko, CY | - |
dc.contributor.author | Lin, R | - |
dc.contributor.author | Li, S | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2019-09-10T02:39:21Z | - |
dc.date.available | 2019-09-10T02:39:21Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 10-16 August 2019, p. 1394-1400 | - |
dc.identifier.isbn | 978-0-9992411-4-1 | - |
dc.identifier.uri | http://hdl.handle.net/10722/275281 | - |
dc.description.abstract | Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose MiSC (Mixed Strategies Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments. | - |
dc.language | eng | - |
dc.publisher | International Joint Conferences on Artificial Intelligence. The Proceedings' website is located at https://www.ijcai.org/proceedings/2019/ | - |
dc.relation.ispartof | International Joint Conference on Artificial Intelligence (IJCAI) | - |
dc.subject | Humans and AI: Human Computation and Crowdsourcing | - |
dc.subject | Humans and AI: Human-AI Collaboration | - |
dc.title | MiSC: Mixed Strategies Crowdsourcing | - |
dc.type | Article | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.24963/ijcai.2019/193 | - |
dc.identifier.scopus | eid_2-s2.0-85074946853 | - |
dc.identifier.hkuros | 304920 | - |
dc.identifier.spage | 1394 | - |
dc.identifier.epage | 1400 | - |
dc.publisher.place | United States | - |