# Discriminative and Generative Transformer-based Models For Situation Entity Classification

arXiv: https://arxiv.org/abs/2109.07434 · HTML: https://mlsanity.com/paper/2109.07434/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://mlsanity.com/paper/1909.11299/) (2019)
- [Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://mlsanity.com/paper/2004.04092/) (2020)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [A Comparison of LSTM and BERT for Small Corpus](https://mlsanity.com/paper/2009.05451/) (2020)
- [Generative and Discriminative Text Classification with Recurrent Neural Networks](https://mlsanity.com/paper/1703.01898/) (2017)
- [Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements](https://mlsanity.com/paper/1901.11429/) (2019)
- [Latent-Variable Generative Models for Data-Efficient Text Classification](https://mlsanity.com/paper/1910.00382/) (2019)
- [Building Context-aware Clause Representations for Situation Entity Type Classification](https://mlsanity.com/paper/1809.07483/) (2018)

## Built on by (most cited first, 3 of 3)

- [Generative or Discriminative? Revisiting Text Classification in the Era of Transformers](https://mlsanity.com/paper/2506.12181/) (2025)
- [A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches](https://mlsanity.com/paper/2208.09012/) (2022)
- [Same Text, Different Prediction: Serving-Context Nondeterminism in Text Classifiers](https://mlsanity.com/paper/2610.09111/) (2026)

## Abstract (arXiv)

We re-examine the situation entity (SE) classification task with varying amounts of available training data. We exploit a Transformer-based variational autoencoder to encode sentences into a lower dimensional latent space, which is used to generate the text and learn a SE classifier. Test set and cross-genre evaluations show that when training data is plentiful, the proposed model can improve over the previous discriminative state-of-the-art models. Our approach performs disproportionately better with smaller amounts of training data, but when faced with extremely small sets (4 instances per label), generative RNN methods outperform transformers. Our work provides guidance for future efforts on SE and semantic prediction tasks, and low-label training regimes.