Reply with Sticker: New Dataset and Model for Sticker Retrieval
<|reference_start|>Reply with Sticker: New Dataset and Model for Sticker Retrieval: Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a vivid, tactful, and intuitive way. Existing sticker retrieval research typically retrieves stickers based on context and the current utterance delivered by the user. That is, the stickers serve as a supplement to the current utterance. However, in the real-world scenario, using stickers to express what we want to say rather than as a supplement to our words only is also important. Therefore, in this paper, we create a new dataset for sticker retrieval in conversation, called \textbf{StickerInt}, where stickers are used to reply to previous conversations or supplement our words\footnote{We believe that the release of this dataset will provide a more complete paradigm than existing work for the research of sticker retrieval in the open-domain online conversation.}. Based on the created dataset, we present a simple yet effective framework for sticker retrieval in conversation based on the learning of intention and the cross-modal relationships between conversation context and stickers, coined as \textbf{Int-RA}. Specifically, we first devise a knowledge-enhanced intention predictor to introduce the intention information into the conversation representations. Subsequently, a relation-aware sticker selector is devised to retrieve the response sticker via cross-modal relationships. Extensive experiments on the created dataset show that the proposed model achieves state-of-the-art performance in sticker retrieval\footnote{The dataset and source code of this work are released at \url{https://github.com/HITSZ-HLT/Int-RA}.}.<|reference_end|>
@article{liang2024reply,
    title={Reply with Sticker: New Dataset and Model for Sticker Retrieval},
    author={Bin Liang, Bingbing Wang, Zhixin Bai, Qiwei Lang, Mingwei Sun, Kaiheng
  Hou, Lanjun Zhou, Ruifeng Xu, Kam-Fai Wong},
    journal={arXiv preprint arXiv:2403.05427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05427},
    primaryClass={cs.MM}
}