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

Autor:	Chan, Shih-Han, Yang, Tsai-Lun, Chu, Yun-Wei, Hsu, Chi-Yang, Huang, Ting-Hao, Chiu, Yu-Shian, Ku, Lun-Wei
Rok vydání:	2022
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as a full talk paper on AAAI-DEEPDIAL'21
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.09327 Zobrazit plný text záznamu View this record from Arxiv