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

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.
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.
Writing Polishment with Simile: Task, Dataset and A Neural Approach
http://arxiv.org/abs/2012.08117v1