# Selecting Stickers in Open-Domain Dialogue through Multitask Learning

arXiv: https://arxiv.org/abs/2209.07697 · HTML: https://mlsanity.com/paper/2209.07697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.06585/) (2017)
- [Maria: A Visual Experience Powered Conversational Agent](https://mlsanity.com/paper/2105.13073/) (2021)
- [Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog](https://mlsanity.com/paper/2003.04679/) (2020)
- [Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark](https://mlsanity.com/paper/2109.01839/) (2021)
- [Multimodal Emoji Prediction](https://mlsanity.com/paper/1803.02392/) (2018)
- [Are Emojis Predictable?](https://mlsanity.com/paper/1702.07285/) (2017)
- [Understanding Chat Messages for Sticker Recommendation in Messaging Apps](https://mlsanity.com/paper/1902.02704/) (2019)
- [Neural Emoji Recommendation in Dialogue Systems](https://mlsanity.com/paper/1612.04609/) (2016)
- [An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog](https://mlsanity.com/paper/2109.12212/) (2021)

## Built on by (most cited first, 1 of 1)

- [STICKERCONV: Generating Multimodal Empathetic Responses from Scratch](https://mlsanity.com/paper/2402.01679/) (2024)

## Abstract (arXiv)

With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a comprehensive understanding of both dialogues and stickers, as well as the relationship between the two types of modalities. To tackle these challenges, we propose a multitask learning method comprised of three auxiliary tasks to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. Extensive experiments conducted on a recent challenging dataset show that our model can better combine the multimodal information and achieve significantly higher accuracy over strong baselines. Ablation study further verifies the effectiveness of each auxiliary task. Our code is available at urlhttps://github.com/nonstopfor/Sticker-Selection