Query Generation for Multimodal Documents
Kyungho Kim, Kyungjae Lee, Seung-won Hwang, Young-In Song, Seungwook Lee
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- Anthology ID:
- 2021.eacl-main.54
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 659–668
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.54
- DOI:
- 10.18653/v1/2021.eacl-main.54
- Cite (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.
- Cite (Informal):
- Query Generation for Multimodal Documents (Kim et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.eacl-main.54.pdf