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        <title>A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models</title>
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    <name type="personal">
        <namePart type="given">Jaewook</namePart>
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        <namePart type="given">Seongsik</namePart>
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    <name type="personal">
        <namePart type="given">Hongjin</namePart>
        <namePart type="family">Kim</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Most research on multimodal open-domain dialogue agents has focused on pretraining and multi-task learning using additional rich datasets beyond a given target dataset. However, methods for exploiting these additional datasets can be quite limited in real-world settings, creating a need for more efficient methods for constructing agents based solely on the target dataset. To address these issues, we present a new learning strategy called vision-language warm-up tasks for multimodal dialogue models (VLAW-MDM). This strategy does not require the use of large pretraining or multi-task datasets but rather relies solely on learning from target data. Moreover, our proposed approach automatically generate captions for images and incorporate them into the model’s input to improve the contextualization of visual information. Using this novel approach, we empirically demonstrate that our learning strategy is effective for limited data and relatively small models. The result show that our method achieved comparable and in some cases superior performance compared to existing state-of-the-art models on various evaluation metrics.</abstract>
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        <url>https://aclanthology.org/2023.emnlp-main.167</url>
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        <date>2023-12</date>
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            <start>2789</start>
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%0 Conference Proceedings
%T A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models
%A Lee, Jaewook
%A Park, Seongsik
%A Park, Seong-Heum
%A Kim, Hongjin
%A Kim, Harksoo
%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 lee-etal-2023-framework
%X Most research on multimodal open-domain dialogue agents has focused on pretraining and multi-task learning using additional rich datasets beyond a given target dataset. However, methods for exploiting these additional datasets can be quite limited in real-world settings, creating a need for more efficient methods for constructing agents based solely on the target dataset. To address these issues, we present a new learning strategy called vision-language warm-up tasks for multimodal dialogue models (VLAW-MDM). This strategy does not require the use of large pretraining or multi-task datasets but rather relies solely on learning from target data. Moreover, our proposed approach automatically generate captions for images and incorporate them into the model’s input to improve the contextualization of visual information. Using this novel approach, we empirically demonstrate that our learning strategy is effective for limited data and relatively small models. The result show that our method achieved comparable and in some cases superior performance compared to existing state-of-the-art models on various evaluation metrics.
%U https://aclanthology.org/2023.emnlp-main.167
%P 2789-2799

Markdown (Informal)

[A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models](https://aclanthology.org/2023.emnlp-main.167) (Lee et al., EMNLP 2023)

• A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models (Lee et al., EMNLP 2023)

ACL

• Jaewook Lee, Seongsik Park, Seong-Heum Park, Hongjin Kim, and Harksoo Kim. 2023. A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2789–2799, Singapore. Association for Computational Linguistics.