Reflections on Structured Common Sense in an Era of Machine Learning
Catherine Havasi · 2019
Crowdsourcing common sense training data was born twenty years ago. It began with the idea to "harness the power of bored people on the Internet" to collect "what everyone knows but no one writes down". This was an era when we were all just starting to learn how to search the web, before people learned the dismal art of keywords, they tried typing their wants and needs. Search engines were woefully unequipped for these kinds of queries, and it was in that climate that we started ConceptNet, which we originally called OpenMind CommonSense (OMCS). Over the years, the effort and its methods evolved to address new applications. Today, we explore how structured common sense is becoming more relevant in NLP and the role it can play in helping solve problems of explainability, scalability, low-resourced languages, domain transfer, and AI bias. We look at how structured common sense and transformers are complementary and how we can combine them to keep the best of both worlds. Additionally, how does common sense need to evolve as we tackle larger and more complex and cross domain problems?