A Surprisingly Robust Trick for Winograd Schema Challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, Thomas Lukasiewicz
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/vid-koci/bert-commonsenseOfficialIn paperpytorch★ 0
- github.com/TangJiaLong/Knowledge-Projection-for-EREpytorch★ 19
Abstract
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).
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Winograd Schema Challenge | BERTwiki 340M (fine-tuned on WSCR) | Accuracy | 72.5 | — | Unverified |
| Winograd Schema Challenge | BERT-large 340M (fine-tuned on WSCR) | Accuracy | 71.4 | — | Unverified |
| Winograd Schema Challenge | BERTwiki 340M (fine-tuned on half of WSCR) | Accuracy | 70.3 | — | Unverified |
| Winograd Schema Challenge | BERT-base 110M (fine-tuned on WSCR) | Accuracy | 62.3 | — | Unverified |