A Simple Method for Commonsense Reasoning
This addresses the long-standing challenge of commonsense reasoning for AI systems, offering a simple and effective approach that outperforms prior methods.
The authors tackled commonsense reasoning in deep learning by using unsupervised language models to score multiple-choice questions, achieving large-margin improvements over previous state-of-the-art methods on Pronoun Disambiguation and Winograd Schema challenges without annotated knowledge bases or hand-engineered features.
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.