@inproceedings{kim-etal-2021-query,
title = "Query Generation for Multimodal Documents",
author = "Kim, Kyungho and
Lee, Kyungjae and
Hwang, Seung-won and
Song, Young-In and
Lee, Seungwook",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.54",
doi = "10.18653/v1/2021.eacl-main.54",
pages = "659--668",
abstract = "This paper studies the problem of generatinglikely queries for multimodal documents withimages. Our application scenario is enablingefficient {``}first-stage retrieval{''} of relevant doc-uments, by attaching generated queries to doc-uments before indexing. We can then indexthis expanded text to efficiently narrow downto candidate matches using inverted index, sothat expensive reranking can follow. Our eval-uation results show that our proposed multi-modal representation meaningfully improvesrelevance ranking. More importantly, ourframework can achieve the state of the art inthe first stage retrieval scenarios",
}
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<abstract>This paper studies the problem of generatinglikely queries for multimodal documents withimages. Our application scenario is enablingefficient “first-stage retrieval” of relevant doc-uments, by attaching generated queries to doc-uments before indexing. We can then indexthis expanded text to efficiently narrow downto candidate matches using inverted index, sothat expensive reranking can follow. Our eval-uation results show that our proposed multi-modal representation meaningfully improvesrelevance ranking. More importantly, ourframework can achieve the state of the art inthe first stage retrieval scenarios</abstract>
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%0 Conference Proceedings
%T Query Generation for Multimodal Documents
%A Kim, Kyungho
%A Lee, Kyungjae
%A Hwang, Seung-won
%A Song, Young-In
%A Lee, Seungwook
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F kim-etal-2021-query
%X This paper studies the problem of generatinglikely queries for multimodal documents withimages. Our application scenario is enablingefficient “first-stage retrieval” of relevant doc-uments, by attaching generated queries to doc-uments before indexing. We can then indexthis expanded text to efficiently narrow downto candidate matches using inverted index, sothat expensive reranking can follow. Our eval-uation results show that our proposed multi-modal representation meaningfully improvesrelevance ranking. More importantly, ourframework can achieve the state of the art inthe first stage retrieval scenarios
%R 10.18653/v1/2021.eacl-main.54
%U https://aclanthology.org/2021.eacl-main.54
%U https://doi.org/10.18653/v1/2021.eacl-main.54
%P 659-668
Markdown (Informal)
[Query Generation for Multimodal Documents](https://aclanthology.org/2021.eacl-main.54) (Kim et al., EACL 2021)
ACL
- Kyungho Kim, Kyungjae Lee, Seung-won Hwang, Young-In Song, and Seungwook Lee. 2021. Query Generation for Multimodal Documents. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 659–668, Online. Association for Computational Linguistics.