# Deep Multi-Task Learning with Shared Memory

arXiv: https://arxiv.org/abs/1609.07222 · HTML: https://mlsanity.com/paper/1609.07222/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Multi-Task Time Series Forecasting With Shared Attention](https://mlsanity.com/paper/2101.09645/) (2021)
- [Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media](https://mlsanity.com/paper/2101.11155/) (2021)
- [Preliminary Steps Towards Federated Sentiment Classification](https://mlsanity.com/paper/2107.11956/) (2021)
- [Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge](https://mlsanity.com/paper/2105.11902/) (2021)

## Abstract (arXiv)

Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we propose two deep architectures which can be trained jointly on multiple related tasks. More specifically, we augment neural model with an external memory, which is shared by several tasks. Experiments on two groups of text classification tasks show that our proposed architectures can improve the performance of a task with the help of other related tasks.