# Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses

arXiv: https://arxiv.org/abs/2004.10972 · HTML: https://mlsanity.com/paper/2004.10972/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

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

- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests](https://mlsanity.com/paper/2101.06351/) (2021)
- [Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax](https://mlsanity.com/paper/2105.13608/) (2021)
- [Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge](https://mlsanity.com/paper/2305.03287/) (2023)
- [Structure-Preserving Document-Level Data Augmentation via Discourse Parsing](https://mlsanity.com/paper/2610.19497/) (2026)

## Abstract (arXiv)

We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sentence and employs back translation techniques to paraphrase given sentences as augmented data. For labeled sentences, we performed data augmentations to uniform the label distributions and computed supervised loss during training process. For unlabeled sentences, we explored self-training by regarding low-entropy predictions over unlabeled sentences as pseudo labels, assuming high-confidence predictions as labeled data for training. We further introduced consistency regularization as unsupervised loss after data augmentations on unlabeled data, based on the assumption that the model should predict similar class distributions with original unlabeled sentences as input and augmented sentences as input. Via a set of experiments, we demonstrated that our system outperformed baseline models in terms of F1-score and accuracy.