Rafael S. Rezende
2024
Retrieval Evaluation for Long-Form and Knowledge-Intensive Image–Text Article Composition
Jheng-Hong Yang
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Carlos Lassance
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Rafael S. Rezende
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Krishna Srinivasan
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Stéphane Clinchant
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Jimmy Lin
Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia
This paper examines the integration of images into Wikipedia articles by evaluating image–text retrieval tasks in multimedia content creation, focusing on developing retrieval-augmented tools to enhance the creation of high-quality multimedia articles. Despite ongoing research, the interplay between text and visuals, such as photos and diagrams, remains underexplored, limiting support for real-world applications. We introduce AToMiC, a dataset for long-form, knowledge-intensive image–text retrieval, detailing its task design, evaluation protocols, and relevance criteria.Our findings show that a hybrid approach combining a sparse retriever with a dense retriever achieves satisfactory effectiveness, with nDCG@10 scores around 0.4 for Image Suggestion and Image Promotion tasks, providing insights into the challenges of retrieval evaluation in an image–text interleaved article composition context.The AToMiC dataset is available at https://github.com/TREC-AToMiC/AToMiC.
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