Abstract
Commonsense knowledge, such as knowing that “bumping into people annoys them” or “rain makes the road slippery”, helps humans navigate everyday situations seamlessly. Yet, endowing machines with such human-like commonsense reasoning capabilities has remained an elusive goal of artificial intelligence research for decades. In recent years, commonsense knowledge and reasoning have received renewed attention from the natural language processing (NLP) community, yielding exploratory studies in automated commonsense understanding. We organize this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research. In our tutorial, we will (1) outline the various types of commonsense (e.g., physical, social), and (2) discuss techniques to gather and represent commonsense knowledge, while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). We will then (3) discuss the types of commonsense knowledge captured by modern NLP systems (e.g., large pretrained language models), and (4) present ways to measure systems’ commonsense reasoning abilities. We will finish with (5) a discussion of various ways in which commonsense reasoning can be used to improve performance on NLP tasks, exemplified by an (6) interactive session on integrating commonsense into a downstream task.
Highlights
Commonsense knowledge, such as knowing that “bumping into people annoys them” or “rain makes the road slippery”, helps humans navigate everyday situations seamlessly (Apperly, 2010)
Current methods are still not powerful or robust enough to be deployed in open-domain production settings, despite the clear improvements provided by largescale pretrained language models
This shortcoming is partially due to inadequacy in acquiring, understanding and reasoning about commonsense knowledge, topics which remain understudied by the larger natural language processing (NLP), AI, and Vision communities relative to its importance in building AI agents
Summary
Commonsense knowledge, such as knowing that “bumping into people annoys them” or “rain makes the road slippery”, helps humans navigate everyday situations seamlessly (Apperly, 2010). Recent advances in large pretrained language models (e.g., Devlin et al, 2019; Liu et al, 2019b), have pushed machines closer to humanlike understanding capabilities, calling into question whether machines should directly model commonsense through symbolic integrations Despite these impressive performance improvements in a variety of NLP tasks, it remains unclear whether these models are performing complex reasoning, or if they are merely learning complex surface correlation patterns (Davis and Marcus, 2015; Marcus, 2018). Current methods are still not powerful or robust enough to be deployed in open-domain production settings, despite the clear improvements provided by largescale pretrained language models This shortcoming is partially due to inadequacy in acquiring, understanding and reasoning about commonsense knowledge, topics which remain understudied by the larger NLP, AI, and Vision communities relative to its importance in building AI agents. We will (3) discuss the types of commonsense knowledge captured by modern NLP systems (e.g., large pretrained language models), (4) review ways to incorporate commonsense knowledge into downstream task models, and (5) present various benchmarks used to measure systems’ commonsense reasoning abilities
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