# Multi-task dialog act and sentiment recognition on Mastodon

arXiv: https://arxiv.org/abs/1807.05013 · HTML: https://mlsanity.com/paper/1807.05013/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 15 of 15)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations](https://mlsanity.com/paper/2111.06647/) (2021)
- [CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI](https://mlsanity.com/paper/2205.14727/) (2022)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2203.03856/) (2022)
- [Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding](https://mlsanity.com/paper/2306.09114/) (2023)
- [Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process](https://mlsanity.com/paper/2305.12594/) (2023)
- [Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance](https://mlsanity.com/paper/2409.08963/) (2024)
- [Modeling Intention, Emotion and External World in Dialogue Systems](https://mlsanity.com/paper/2202.06476/) (2022)
- [From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue](https://mlsanity.com/paper/2308.12648/) (2023)
- [A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2308.04424/) (2023)
- [A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis](https://mlsanity.com/paper/2410.09556/) (2024)
- [Multi-Task Learning for Depression Detection in Dialogs](https://mlsanity.com/paper/2208.10250/) (2022)
- [Understanding Emotion Valence is a Joint Deep Learning Task](https://mlsanity.com/paper/2305.17422/) (2023)

## Abstract (arXiv)

Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.