# Augmenting NLP data to counter Annotation Artifacts for NLI Tasks

arXiv: https://arxiv.org/abs/2302.04700 · HTML: https://mlsanity.com/paper/2302.04700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Towards Robustifying NLI Models Against Lexical Dataset Biases](https://mlsanity.com/paper/2005.04732/) (2020)

## Abstract (arXiv)

In this paper, we explore Annotation Artifacts - the phenomena wherein large pre-trained NLP models achieve high performance on benchmark datasets but do not actually "solve" the underlying task and instead rely on some dataset artifacts (same across train, validation, and test sets) to figure out the right answer. We explore this phenomenon on the well-known Natural Language Inference task by first using contrast and adversarial examples to understand limitations to the model's performance and show one of the biases arising from annotation artifacts (the way training data was constructed by the annotators). We then propose a data augmentation technique to fix this bias and measure its effectiveness.