Writing Polishment with Simile: Task, Dataset and A Neural Approach

Authors

  • Jiayi Zhang Xiaomi AI Lab
  • Zhi Cui Xiaomi AI Lab
  • Xiaoqiang Xia Xiaomi AI Lab
  • Yalong Guo Xiaomi AI Lab
  • Yanran Li Xiaomi AI Lab
  • Chen Wei Xiaomi AI Lab
  • Jianwei Cui Xiaomi AI Lab

Keywords:

Generation, Language Models, Applications

Abstract

A simile is a figure of speech that directly makes a comparison, showing similarities between two different things, e.g. ``Reading papers can be dull sometimes,like watching grass grow". Human writers often interpolate appropriate similes into proper locations of the plain text to vivify their writings. However, none of existing work has explored neural simile interpolation, including both locating and generation. In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Accordingly, we design a two-staged Locate&Gen model based on transformer architecture. Our model firstly locates where the simile interpolation should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile (CS) dataset containing 5 million similes with context. The experimental results demonstrate the feasibility of WPS task and shed light on the future research directions towards better automatic text polishment.

Downloads

Published

2021-05-18

How to Cite

Zhang, J., Cui, Z., Xia, X., Guo, Y., Li, Y., Wei, C., & Cui, J. (2021). Writing Polishment with Simile: Task, Dataset and A Neural Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14383-14392. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17691

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III