Smart Word Suggestions for Writing Assistance
Chenshuo Wang Shaoguang Mao Tao Ge Wenshan Wu Xun Wang Yan Xia Jonathan Tien Dongyan Zhao
Enhancing word usage is a desired feature for writing assistance. To further
advance research in this area, this paper introduces "Smart Word Suggestions"
(SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end
evaluation and presents a more realistic writing assistance scenario. This task
involves identifying words or phrases that require improvement and providing
substitution suggestions. The benchmark includes human-labeled data for
testing, a large distantly supervised dataset for training, and the framework
for evaluation. The test data includes 1,000 sentences written by English
learners, accompanied by over 16,000 substitution suggestions annotated by 10
native speakers. The training dataset comprises over 3.7 million sentences and
12.7 million suggestions generated through rules. Our experiments with seven
baselines demonstrate that SWS is a challenging task. Based on experimental
analysis, we suggest potential directions for future research on SWS. The
dataset and related codes is available at
https://github.com/microsoft/SmartWordSuggestions.