Papers › A Simple Method for Commonsense Reasoning
A Simple Method for Commonsense Reasoning
Trieu H. Trinh, Quoc V. Le
Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Coreference Resolution | Winograd Schema Challenge | Ensemble of 14 LMs | Accuracy | 63.7 | #45 of 82 | Archive leaderboard | report |
| Coreference Resolution | Winograd Schema Challenge | Word-level CNN+LSTM (partial scoring) | Accuracy | 62.6 | #49 of 82 | Archive leaderboard | report |
| Coreference Resolution | Winograd Schema Challenge | Char-level CNN+LSTM (partial scoring) | Accuracy | 57.9 | #64 of 82 | Archive leaderboard | report |
| Natural Language Understanding | PDP60 | Word-level CNN+LSTM (full scoring) | Accuracy | 60.0 | #9 of 13 | Archive leaderboard | report |
| Natural Language Understanding | PDP60 | Word-level CNN+LSTM (partial scoring) | Accuracy | 53.3 | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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