# Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

arXiv: https://arxiv.org/abs/1802.09913 · HTML: https://mlsanity.com/paper/1802.09913/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 11 of 11)

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- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis](https://mlsanity.com/paper/2107.11435/) (2021)
- [Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2208.08280/) (2022)
- [MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces](https://mlsanity.com/paper/2109.04240/) (2021)
- [Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories](https://mlsanity.com/paper/2110.12113/) (2021)
- [Incorporating Emotions into Health Mention Classification Task on Social Media](https://mlsanity.com/paper/2212.05039/) (2022)
- [Multi-task Learning for Personal Health Mention Detection on Social Media](https://mlsanity.com/paper/2212.05147/) (2022)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)

## Abstract (arXiv)

We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for topic-based sentiment analysis.