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.

通过大型语言模型的帮助，Emojinize 方法能够将任意文本短语翻译为一个或多个表情符号序列，以增强其表达能力。经过用户研究，我们发现 Emojinize 的表情符号翻译能够提升掩盖词的猜测性，表情符号的选取比人工选择的表情符号翻译提高了更多猜测准确率。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各种单词。此外，使用 Emojinize 的表情符号翻译对于儿童学习阅读、成人学习外语以及阅读障碍人群的文本理解等具有广泛的应用前景。