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Query generation for multimodal documents

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Issue Date
2021-04
Publisher
Association for Computational Linguistics (ACL)
Citation
EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp.659-668
Abstract
© 2021 Association for Computational LinguisticsThis paper studies the problem of generating likely queries for multimodal documents with images. Our application scenario is enabling efficient “first-stage retrieval” of relevant documents, by attaching generated queries to documents before indexing. We can then index this expanded text to efficiently narrow down to candidate matches using inverted index, so that expensive reranking can follow. Our evaluation results show that our proposed multimodal representation meaningfully improves relevance ranking. More importantly, our framework can achieve the state of the art in the first-stage retrieval scenarios.
URI
https://hdl.handle.net/10371/183755
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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