Emoji have become ubiquitous in written communication, on the Web and beyond. They can emphasize or clarify emotions, add details to conversations, or simply serve decorative purposes. This casual use, however, barely scratches the surface of the expressive power of emoji. To further unleash this power, we present Emojinize, a method for translating arbitrary text phrases into sequences of one or more emoji without requiring human input. By leveraging the power of large language models, Emojinize can choose appropriate emoji by disambiguating based on context (eg, cricket-bat vs bat) and can express complex concepts compositionally by combining multiple emoji (eq, ''Emojinize'' is translated to input-latin-letters right-arrow grinning-face). In a cloze test--based user study, we show that Emojinize's emoji translations increase the human guessability of masked words by 55%, whereas human-picked emoji translations do so by only 29%. These results suggest that emoji provide a sufficiently rich vocabulary to accurately translate a wide variety of words. Moreover, annotating words and phrases with Emojinize's emoji translations opens the door to numerous downstream applications, including children learning how to read, adults learning foreign languages, and text understanding for people with learning disabilities.

翻译：表情符号在书面交流、网络及其他领域已变得无处不在。它们可以强调或澄清情绪、为对话增添细节，或仅用于装饰目的。然而，这种随意使用仅仅触及了表情符号表达潜力的皮毛。为进一步释放这种潜力，我们提出了Emojinize——一种无需人工输入即可将任意文本短语翻译为一个或多个表情符号序列的方法。通过利用大型语言模型的能力，Emojinize能够根据上下文消除歧义（例如，区分“板球拍”与“蝙蝠”），并能够通过组合多个表情符号（例如，“Emojinize”被翻译为input-latin-letters右箭头咧嘴笑）来组合表达复杂概念。在基于完形填空测试的用户研究中，我们证明：Emojinize的表情符号翻译使人类对掩码词语的猜测准确率提升55%，而人类挑选的表情符号翻译仅提升29%。这些结果表明，表情符号提供了足够丰富的词汇来准确翻译多种多样的词语。此外，用Emojinize的表情符号翻译标注词语和短语，为众多下游应用打开了大门，包括儿童学习阅读、成人学习外语以及帮助有学习障碍者理解文本。