A Surprisingly Robust Trick for Winograd Schema Challenge
This work addresses commonsense reasoning benchmarks for natural language understanding, showing incremental improvements in model performance and robustness.
The paper tackles the Winograd Schema Challenge by fine-tuning BERT on a similar pronoun disambiguation dataset and an unsupervised generated dataset, achieving state-of-the-art accuracies of 72.5% on WSC273 and 74.7% on WNLI, with improvements of 8.8% and 9.6% respectively.
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 strongly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.5% and 74.7% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8% and 9.6%, respectively. Furthermore, our fine-tuned models are also consistently more robust on the "complex" subsets of WSC273, introduced by Trichelair et al. (2018).