ConvoSense: Overcoming Monotonous Commonsense Inferences for
  Conversational AI
Sarah E. Finch Jinho D. Choi
Mastering commonsense understanding and reasoning is a pivotal skill
essential for conducting engaging conversations. While there have been several
attempts to create datasets that facilitate commonsense inferences in dialogue
contexts, existing datasets tend to lack in-depth details, restate information
already present in the conversation, and often fail to capture the multifaceted
nature of commonsense reasoning. In response to these limitations, we compile a
new synthetic dataset for commonsense reasoning in dialogue contexts using GPT,
ConvoSense, that boasts greater contextual novelty, offers a higher volume of
inferences per example, and substantially enriches the detail conveyed by the
inferences. Our dataset contains over 500,000 inferences across 12,000
dialogues with 10 popular inference types, which empowers the training of
generative commonsense models for dialogue that are superior in producing
plausible inferences with high novelty when compared to models trained on the
previous datasets. To the best of our knowledge, ConvoSense is the first of its
kind to provide such a multitude of novel inferences at such a large scale.