WordDecipher: Enhancing Digital Workspace Communication with Explainable
  AI for Non-native English Speakers
Yuexi Chen Zhicheng Liu
Non-native English speakers (NNES) face challenges in digital workspace
communication (e.g., emails, Slack messages), often inadvertently translating
expressions from their native languages, which can lead to awkward or incorrect
usage. Current AI-assisted writing tools are equipped with fluency enhancement
and rewriting suggestions; however, NNES may struggle to grasp the subtleties
among various expressions, making it challenging to choose the one that
accurately reflects their intent. Such challenges are exacerbated in high-stake
text-based communications, where the absence of non-verbal cues heightens the
risk of misinterpretation. By leveraging the latest advancements in large
language models (LLM) and word embeddings, we propose WordDecipher, an
explainable AI-assisted writing tool to enhance digital workspace communication
for NNES. WordDecipher not only identifies the perceived social intentions
detected in users' writing, but also generates rewriting suggestions aligned
with users' intended messages, either numerically or by inferring from users'
writing in their native language. Then, WordDecipher provides an overview of
nuances to help NNES make selections. Through a usage scenario, we demonstrate
how WordDecipher can significantly enhance an NNES's ability to communicate her
request, showcasing its potential to transform workspace communication for
NNES.