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Sticker Response Selector

SRS

45 papers tagged archive 2025-07-28

Introduced by Shen Gao et al. in Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Sticker Response Selector, or SRS, is a model for multi-turn dialog that automatically selects a sticker response. SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score.

PaperSource

Papers archive 2025-07-28

30 shown of 45, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 63 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Recommendation Systems21
Sequential Recommendation16
Data Augmentation4
Reinforcement Learning2
Semantic Communication2
reinforcement-learning2
Adversarial Attack1
Attribute1
Automatic Speech Recognition1
BIG-bench Machine Learning1
Bayesian Optimization1
CPU1
Clustering1
Collaborative Filtering1
Computed Tomography (CT)1
Contrastive Learning1
Data Poisoning1
Decision Making1
Deep Reinforcement Learning1
Denoising1

Usage over time archive 2025-07-28

Papers per year tagged with SRS: 2020 to 2025, peak 17 17 0 2020: 1 paper 2020 2021: 2 papers 2021 2022: 3 papers 2022 2023: 16 papers 2023 2024: 17 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (45 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Conversational Models

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