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        <title>Cultural Concept Adaptation on Multimodal Reasoning</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Houda</namePart>
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    <abstract>Developing cultural adaptation methods is important, which can improve the model performance on the low-resource ones and provide more equitable opportunities for everyone to benefit from advanced technology. Past methods primarily focused on multilingual and multimodal capabilities, and the improvement of multicultural competence is still an unexplored problem. This is largely due to the difficulty of data scarcity and expensive annotation. In this paper, we navigate this uncharted territory by leveraging high-resource cultures to facilitate comprehension of low-resource ones. We first introduce an annotation-free method for cultural-concept adaptation and construct a concept mapping set. To facilitate the model’s comprehension of cultural-concept mappings, we propose a new multimodal data augmentation called CultureMixup. This approach employs a three-tier code-switching strategy on textual sentences. Additionally, it uses a cultural concept-based mixup method for the images. This combination effectively generates new data instances across culture, phrase, word, and image levels. For visually grounded reasoning across languages and cultures, experimental results on five languages show that our method consistently improves performance for four existing multilingual and multimodal models on both zero-shot and few-shot settings.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.18</identifier>
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%0 Conference Proceedings
%T Cultural Concept Adaptation on Multimodal Reasoning
%A Li, Zhi
%A Zhang, Yin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-zhang-2023-cultural
%X Developing cultural adaptation methods is important, which can improve the model performance on the low-resource ones and provide more equitable opportunities for everyone to benefit from advanced technology. Past methods primarily focused on multilingual and multimodal capabilities, and the improvement of multicultural competence is still an unexplored problem. This is largely due to the difficulty of data scarcity and expensive annotation. In this paper, we navigate this uncharted territory by leveraging high-resource cultures to facilitate comprehension of low-resource ones. We first introduce an annotation-free method for cultural-concept adaptation and construct a concept mapping set. To facilitate the model’s comprehension of cultural-concept mappings, we propose a new multimodal data augmentation called CultureMixup. This approach employs a three-tier code-switching strategy on textual sentences. Additionally, it uses a cultural concept-based mixup method for the images. This combination effectively generates new data instances across culture, phrase, word, and image levels. For visually grounded reasoning across languages and cultures, experimental results on five languages show that our method consistently improves performance for four existing multilingual and multimodal models on both zero-shot and few-shot settings.
%R 10.18653/v1/2023.emnlp-main.18
%U https://aclanthology.org/2023.emnlp-main.18
%U https://doi.org/10.18653/v1/2023.emnlp-main.18
%P 262-276

Markdown (Informal)

[Cultural Concept Adaptation on Multimodal Reasoning](https://aclanthology.org/2023.emnlp-main.18) (Li & Zhang, EMNLP 2023)

• Cultural Concept Adaptation on Multimodal Reasoning (Li & Zhang, EMNLP 2023)

ACL

• Zhi Li and Yin Zhang. 2023. Cultural Concept Adaptation on Multimodal Reasoning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 262–276, Singapore. Association for Computational Linguistics.