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.

通过利用最新的大型语言模型和词嵌入技术，我们提出了WordDecipher，这是一个可解释的AI辅助写作工具，可以增强非母语英语使用者在数字工作空间中的沟通。WordDecipher不仅可以识别用户写作中的社交意图，还可以生成与用户意图相一致的重写建议，并从用户的母语写作中推断。然后，WordDecipher提供了细微差别的概述，以帮助非母语使用者进行选择。通过使用场景，我们展示了WordDecipher如何显著增强非母语使用者在工作空间中沟通的能力，展示了其改变非母语使用者工作空间沟通的潜力。

WordDecipher：用可解释的AI增强非英语母语者的数字工作区通信