PIQA: Reasoning about Physical Commonsense in Natural Language
Yonatan Bisk Rowan Zellers Ronan Le Bras Jianfeng Gao Yejin Choi Affiliation: Allen Institute for Artificial Intelligence Microsoft Research AI Carnegie Mellon University Affiliation: Paul G. Allen School for Computer Science and Engineering, University of Washingtonhttp://yonatanbisk.com/piqa Affiliation: Paul G. Allen School for Computer Science and Engineering, University of Washingtonhttp://yonatanbisk.com/piqa Affiliation: Paul G. Allen School for Computer Science and Engineering, University of Washingtonhttp://yonatanbisk.com/piqa
Abstract
To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today’s natural language understanding systems. While recent pretrained models (such as BERT) have made progress on question answering over more abstract domains – such as news articles and encyclopedia entries, where text is plentiful – in more physical domains, text is inherently limited due to reporting bias. Can AI systems learn to reliably answer physical commonsense questions without experiencing the physical world?
原文 arXiv:1911.11641;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1911.11641v1