Structural and Functional Decomposition for Personality Image Captioning in a Communication Game

Abstract

Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener. The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits. In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits. In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC. This enables the speaker and listener to benefit from the language encoding capacity of GPT2. Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.

arXiv comment10 pages, EMNLP-Findings 2020

References in corpus (5)

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Personalizing Dialogue Agents: I have a dog, do you have pets too?
• Discriminability objective for training descriptive captions
• Neural Joking Machine : Humorous image captioning