Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data

TL;DR

Counterfactually-augmented SNLI training data does not improve model generalization or robustness compared to unaugmented data, and may even reduce performance, indicating the need for new data augmentation methods.

Contribution

This study evaluates the effectiveness of counterfactual data augmentation on natural language inference and finds it does not enhance model generalization or robustness.

Findings

Counterfactual augmentation does not improve out-of-domain generalization.

Models trained on augmented data can perform worse on challenge examples.

Standard crowdsourcing methods for augmentation are ineffective for NLI datasets.

Abstract

A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks---datasets collected from crowdworkers to create an evaluation task---while still failing on out-of-domain examples for the same task. Recent work has explored the use of counterfactually-augmented data---data built by minimally editing a set of seed examples to yield counterfactual labels---to augment training data associated with these benchmarks and build more robust classifiers that generalize better. However, Khashabi et al. (2020) find that this type of augmentation yields little benefit on reading comprehension tasks when controlling for dataset size and cost of collection. We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactually-augmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples. Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.