[2L4-J-9] Natural language processing, information retrieval: conversion and generation

[2L4-J-9-04] Generating Emoji with Conditional Variational Autoencoders and Word Embedding

〇Atsuki Yamaguchi¹, Katsuhide Fujita¹ (1. Tokyo University of Agriculture and Technology)

Keywords:Emoji synthesis, Conditional variational autoencoders, Word embedding, Natural language processing

Emoji are among the most widely used communication tools worldwide. Because the number of emoji increases every year and there are 82 face emoji, it might be difficult for users to select an appropriate emoji immediately. Moreover, it is troublesome to continue designing new emoji. Therefore, the aim of the present study is to generate an emoji based on input text automatically to facilitate easier communication and eliminate the process of designing new emoji. The proposed model employs conditional variational autoencoders (CVAE), quasi-recurrent neural networks (QRNN) as the text encoder, and the pre-trained word vector GloVe to the embedding layer connected to the text encoder. In the experiments described herein, it will be observed that the proposed method can generate an emoji that corresponds to an input caption, and output image quality is improved using GloVe.