Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets

• URL: http://arxiv.org/abs/2010.08580v3
• Date: Thu, 12 Nov 2020 18:16:58 GMT
• Title: Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets
• Authors: Chuanrong Li, Lin Shengshuo, Leo Z. Liu, Xinyi Wu, Xuhui Zhou, Shane Steinert-Threlkeld
• Abstract summary: We propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets. Experiments show that current pretrained language models struggle on our automatically generated contrast sets. We improve models' performance on the contrast sets by apply-ing LIT to augment the training data, without affecting performance on the original data.
• Score: 13.706520309917634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often re-quires human-expert annotation, which is expensive and hard to create on a large scale. In this work, we propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets, which enables practitioners to explore linguistic phenomena of interests as well as compose different phenomena. Experimenting with our method on SNLI and MNLI shows that current pretrained language models, although being claimed to contain sufficient linguistic knowledge, struggle on our automatically generated contrast sets. Furthermore, we improve models' performance on the contrast sets by apply-ing LIT to augment the training data, without affecting performance on the original data.

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