New Dataset and Model for Sticker Retrieval

Keskeiset käsitteet

The author introduces a new dataset, StickerInt, and proposes the Int-RA framework for sticker retrieval in conversations. The approach leverages intention information and visual attributes to enhance sticker retrieval performance.

Tiivistelmä

The content discusses the importance of stickers in online conversations and introduces a new dataset, StickerInt, for sticker retrieval. The proposed Int-RA framework utilizes intention information and visual attributes to improve sticker retrieval accuracy. Extensive experiments demonstrate the effectiveness of the approach in outperforming baseline models.
Key Points:

Introduction of StickerInt dataset for comprehensive sticker retrieval.
Proposal of Int-RA framework leveraging intention information and visual attributes.
Experiments showing superior performance compared to baseline models.

Tilastot

Our Stick-
erInt dataset contains 1,578 Chinese conversations with 12,644 utterances.
Extensive experiments on the created dataset show that the proposed model achieves state-of-the-art performance in sticker retrieval.

Lainaukset

"We propose a novel pipeline framework for sticker retrieval."
"Our proposed approach achieves outstanding performance in sticker retrieval."

Tärkeimmät oivallukset

Reply with Sticker

by Bin Liang,Bi... klo arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05427.pdf

Syvällisempiä Kysymyksiä

How can diverse styles of stickers impact the performance of the sticker retrieval task?

The diverse styles of stickers can significantly impact the performance of the sticker retrieval task in several ways. Firstly, different styles may introduce variability in visual features, making it challenging for models to generalize across a wide range of stickers. For instance, cartoon-style stickers may have distinct characteristics compared to realistic or abstract ones, requiring models to adapt their learning mechanisms accordingly. Secondly, diverse styles could lead to ambiguity in interpretation as users might use similar-looking stickers with varying intentions or meanings. This ambiguity can pose challenges for models trying to accurately match stickers with user intentions based on context.

What are potential challenges when dealing with multi-user conversations in sticker retrieval?

Dealing with multi-user conversations introduces several challenges in sticker retrieval tasks. One significant challenge is understanding the dynamics between multiple users and their interactions within a conversation context. Different users may have varied preferences for using stickers or expressing emotions visually, adding complexity to identifying suitable responses. Additionally, determining which user's intention should be prioritized when selecting a response sticker becomes crucial but intricate in multi-user settings.

How can user information be integrated to further enhance the performance of sticker retrieval frameworks?

Integrating user information into sticker retrieval frameworks can enhance performance by providing personalized recommendations tailored to individual preferences and communication styles. User data such as past interactions, preferred types of stickers, or emotional cues expressed through text can help refine the selection process for response stickers. By leveraging this information effectively, models can better predict user intentions and deliver more relevant and engaging suggestions during conversations.