A CNN-Based Tool to Index Emotion on Anime Character Stickers

Jesus, Ivan; Cardoso, Jessica; Busson, Antonio José G.; Guedes, Álan L. V.; Colcher, Sérgio; Milidiú, Ruy Luiz

doi:10.1109/ism46123.2019.00071

Cited by 3 publications

(1 citation statement)

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“…Compared with emoji which is restricted at a fixed size, (Barbieri et al, 2018(Barbieri et al, , 2017, Internet meme is more expressive and of a great variety. One similar work to ours is sticker recommendation (Jesus et al, 2019;Laddha et al, 2020;Gao et al, 2020), where suitable stickers are retrieved to match the text-only dialogue history, which can be regarded as a special case of MOD. Besides, the tasks of meme retrieval (Milo et al, 2019;Perez-Martin et al, 2020;Sharma et al, 2020), detecting the hate speech in memes and clustering memes according to events (Miliani et al, 2020;Kiela et al, 2020) are proposed to help the Internet meme modeling.…”

Section: Internet Meme In Dialoguementioning

confidence: 99%

Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark

Fei¹,

Li²,

Zhang³

et al. 2021

Preprint

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As a kind of new expression elements, Internet memes are popular and extensively used in online chatting scenarios since they manage to make dialogues vivid, moving, and interesting. However, most current dialogue researches focus on text-only dialogue tasks. In this paper, we propose a new task named as Meme incorporated Open-domain Dialogue (MOD). Compared to previous dialogue tasks, MOD is much more challenging since it requires the model to understand the multimodal elements as well as the emotions behind them. To facilitate the MOD research, we construct a large-scale open-domain multimodal dialogue dataset incorporating abundant Internet memes into utterances. The dataset consists of ∼45K Chinese conversations with ∼606K utterances. Each conversation contains about 13 utterances with about 4 Internet memes on average and each utterance equipped with an Internet meme is annotated with the corresponding emotion. In addition, we present a simple and effective method, which utilizes a unified generation network to solve the MOD task. Experimental results demonstrate that our method trained on the proposed corpus is able to achieve expressive communication including texts and memes. The corpus and models have been publicly available at https:// github.com/lizekang/DSTC10-MOD.

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