Conversational Models
Sticker Response Selector
Introduced by Gao et al. in Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn DialogSticker 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.
Source: Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Recommendation Systems | 7 | 21.21% |
| Sequential Recommendation | 5 | 15.15% |
| Time-to-Event Prediction | 1 | 3.03% |
| Uncertainty Quantification | 1 | 3.03% |
| Speech Recognition | 1 | 3.03% |
| Future prediction | 1 | 3.03% |
| Econometrics | 1 | 3.03% |
| Interpretable Machine Learning | 1 | 3.03% |
| Marketing | 1 | 3.03% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
