# LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT

arXiv: https://arxiv.org/abs/2101.04965 · HTML: https://mlsanity.com/paper/2101.04965/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [Comparative Studies of Detecting Abusive Language on Twitter](https://mlsanity.com/paper/1808.10245/) (2018)
- [Multitask Learning for Fine-Grained Twitter Sentiment Analysis](https://mlsanity.com/paper/1707.03569/) (2017)
- [Decision Stream: Cultivating Deep Decision Trees](https://mlsanity.com/paper/1704.07657/) (2017)
- [Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings](https://mlsanity.com/paper/1904.04096/) (2019)

## Abstract (arXiv)

In our paper, we present Deep Learning models with a layer differentiated training method which were used for the SHARED TASK@ CONSTRAINT 2021 sub-tasks COVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We propose a Layer Differentiated training procedure for training a pre-trained ULMFiT arXiv:1801.06146 model. We used special tokens to annotate specific parts of the tweets to improve language understanding and gain insights on the model making the tweets more interpretable. The other two submissions included a modified RoBERTa model and a simple Random Forest Classifier. The proposed approach scored a precision and f1 score of 0.96728972 and 0.967324832 respectively for sub-task "COVID19 Fake News Detection in English". Also, Coarse-Grained Hostility f1 Score and Weighted FineGrained f1 score of 0.908648 and 0.533907 respectively for sub-task Hostile Post Detection in Hindi. The proposed approach ranked 61st out of 164 in the sub-task "COVID19 Fake News Detection in English and 18th out of 45 in the sub-task Hostile Post Detection in Hindi".