Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization

TL;DR

This paper introduces a multi-scale data augmentation approach to mitigate dataset artifacts in natural language inference, improving pre-trained model robustness and performance on challenging NLP tasks.

Contribution

It proposes a novel multi-scale data augmentation method combining sentence-level and word-level techniques to reduce artifacts in NLI datasets and enhance model generalization.

Findings

Enhanced model resistance to perturbation testing.

Improved performance over baseline models.

Effective artifact mitigation in SNLI dataset.

Abstract

Machine learning models can reach high performance on benchmark natural language processing (NLP) datasets but fail in more challenging settings. We study this issue when a pre-trained model learns dataset artifacts in natural language inference (NLI), the topic of studying the logical relationship between a pair of text sequences. We provide a variety of techniques for analyzing and locating dataset artifacts inside the crowdsourced Stanford Natural Language Inference (SNLI) corpus. We study the stylistic pattern of dataset artifacts in the SNLI. To mitigate dataset artifacts, we employ a unique multi-scale data augmentation technique with two distinct frameworks: a behavioral testing checklist at the sentence level and lexical synonym criteria at the word level. Specifically, our combination method enhances our model's resistance to perturbation testing, enabling it to continuously outperform the pre-trained baseline.