Emojinize: Enriching Any Text with Emoji Translations
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Contextual Object Detection with Multimodal Large Language Models
Are Pretrained Multilingual Models Equally Fair Across Languages?
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules
LMSOC: An Approach for Socially Sensitive Pretraining
토론
이 논문과 연결된 포럼 글이 아직 없습니다.