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Let's Talk! Striking Up Conversations via Conversational Visual Question Generation

arXiv 2205.09327 ↗First posted 19 May 2022cs.AI cs.CL cs.CVcited by 0

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Abstract (arXiv)

An engaging and provocative question can open up a great conversation. In this work, we explore a novel scenario: a conversation agent views a set of the user's photos (for example, from social media platforms) and asks an engaging question to initiate a conversation with the user. The existing vision-to-question models mostly generate tedious and obvious questions, which might not be ideals conversation starters. This paper introduces a two-phase framework that first generates a visual story for the photo set and then uses the story to produce an interesting question. The human evaluation shows that our framework generates more response-provoking questions for starting conversations than other vision-to-question baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 19 May 2022.

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23 of 30 references.

References · 23

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11 more
  1. 2016Visual Storytellingcited by 176
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  9. 2019Reinforced Dynamic Reasoning for Conversational Question Generationcited by 15
  10. 2019Question Generation by Transformerscited by 10
  11. 2018Multimodal Differential Network for Visual Question Generationcited by 7

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