Attention Is (not) All You Need for Commonsense Reasoning
Tassilo Klein, Moin Nabi
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Abstract
The recently introduced BERT model exhibits strong performance on several language understanding benchmarks. In this paper, we describe a simple re-implementation of BERT for commonsense reasoning. We show that the attentions produced by BERT can be directly utilized for tasks such as the Pronoun Disambiguation Problem and Winograd Schema Challenge. Our proposed attention-guided commonsense reasoning method is conceptually simple yet empirically powerful. Experimental analysis on multiple datasets demonstrates that our proposed system performs remarkably well on all cases while outperforming the previously reported state of the art by a margin. While results suggest that BERT seems to implicitly learn to establish complex relationships between entities, solving commonsense reasoning tasks might require more than unsupervised models learned from huge text corpora.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Winograd Schema Challenge | BERT-base 110M + MAS | Accuracy | 60.3 | — | Unverified |
| Winograd Schema Challenge | USSM + Supervised DeepNet + KB | Accuracy | 52.8 | — | Unverified |
| Winograd Schema Challenge | USSM + KB | Accuracy | 52 | — | Unverified |