@inproceedings{0c8e0e509b2d4117b0e8ab4c8ccef3f9,
title = "Query generation for multimodal documents",
abstract = "This 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.",
author = "Kyungho Kim and Kyungjae Lee and Hwang, {Seung Won} and Song, {Young In} and Seungwook Lee",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 ; Conference date: 19-04-2021 Through 23-04-2021",
year = "2021",
language = "English",
series = "EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "659--668",
booktitle = "EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference",
address = "United States",
}