Advancing Transformer's Capabilities in Commonsense Reasoning

• URL: http://arxiv.org/abs/2310.06803v1
• Date: Tue, 10 Oct 2023 17:21:03 GMT
• Title: Advancing Transformer's Capabilities in Commonsense Reasoning
• Authors: Yu Zhou, Yunqiu Han, Hanyu Zhou, Yulun Wu
• Abstract summary: We introduce current ML-based methods to improve general purpose pre-trained language models in the task of commonsense reasoning. Our best model outperforms the strongest previous works by 15% absolute gains in Pairwise Accuracy and 8.7% absolute gains in Standard Accuracy.
• Score: 6.5798066703568105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in general purpose pre-trained language models have shown great potential in commonsense reasoning. However, current works still perform poorly on standard commonsense reasoning benchmarks including the Com2Sense Dataset. We argue that this is due to a disconnect with current cutting-edge machine learning methods. In this work, we aim to bridge the gap by introducing current ML-based methods to improve general purpose pre-trained language models in the task of commonsense reasoning. Specifically, we experiment with and systematically evaluate methods including knowledge transfer, model ensemble, and introducing an additional pairwise contrastive objective. Our best model outperforms the strongest previous works by ~15\% absolute gains in Pairwise Accuracy and ~8.7\% absolute gains in Standard Accuracy.

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