Writing Polishment with Simile: Task, Dataset and A Neural Approach
This work addresses the problem of automatically enhancing text with similes for general text generation and natural language processing applications, representing an incremental step in text quality improvement.
This paper introduces a new task called Writing Polishment with Simile (WPS) to enable machines to enhance text with similes. The authors developed a two-stage Locate&Gen model based on a transformer architecture and created a large-scale Chinese Simile (CS) dataset containing 5 million similes with context.
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.