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Evaluating humorous response generation to playful shopping requests

By Natalie Shapira, Oren Kalinsky, Alexander Libov, Chen Shani, Sofia Tolmach
2023
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AI assistants are gradually becoming embedded in our lives, utilized for everyday tasks like shopping or music. In addition to the everyday utilization of AI assistants, many users engage them with playful shopping requests, gauging their ability to understand – or simply seeking amusement. However, these requests are often not being responded to in the same playful manner, causing dissatisfaction and even trust issues. In this work, we focus on equipping AI assistants with the ability to respond in a playful manner to irrational shopping requests. We first evaluate several neural generation models, which lead to unsuitable results – showing that this task is non-trivial. We devise a simple, yet effective, solution, that utilizes a knowledge graph to generate template-based responses grounded with commonsense. While the commonsense-aware solution is slightly less diverse than the generative models, it provides better responses to playful requests. This emphasizes the gap in commonsense exhibited by neural language models.
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