# Text Data Augmentation: Towards better detection of spear-phishing emails

arXiv: https://arxiv.org/abs/2007.02033 · HTML: https://mlsanity.com/paper/2007.02033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [LLM Agents can Autonomously Hack Websites](https://mlsanity.com/paper/2402.06664/) (2024)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://mlsanity.com/paper/2112.02721/) (2021)
- [MixGen: A New Multi-Modal Data Augmentation](https://mlsanity.com/paper/2206.08358/) (2022)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [Unsupervised Document Embedding via Contrastive Augmentation](https://mlsanity.com/paper/2103.14542/) (2021)
- [Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores](https://mlsanity.com/paper/2203.00354/) (2022)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [Investigating Masking-based Data Generation in Language Models](https://mlsanity.com/paper/2307.00008/) (2023)

## Abstract (arXiv)

Text data augmentation, i.e., the creation of new textual data from an existing text, is challenging. Indeed, augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g., Machine Translation, Text Classification). Initially motivated by an application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic augmentation framework used as a service to augment English texts within our company. Our proposal combines different methods, utilizing BERT language model, multi-step back-translation and heuristics. We show that our augmentation framework improves performances on several text classification tasks using publicly available models and corpora as well as on a BEC detection task. We also provide a comprehensive argumentation about the limitations of our augmentation framework.