Emojilingo: Harnessing AI to Translate Words into Emojis
Authors: Francesca Chiusaroli, Federico Sangati, Johanna Monti, Maria Laura Pierucci, Tiberio Uricchio (Università di Macerata, Okinawa Institute of Science and Technology, Università di Napoli L'Orientale, Università di Pisa, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper presents an AI experiment of translation into emoji conducted on a glossary from Dante Alighieri’s Divine Comedy. The experiment is part of a project aiming to build up an automated emoji-based pivot language providing an interlingua as a tool for linguistic simplification, accessibility, and international communication: Emojilingo URL. The present test involves human (Emojitaliano) and machine (Chat-GPT) translations in a comparative analysis in order to devise an automated integrated model highlighting emojis' expressive ability in transferring senses, clarifying semantic obscurities and ambiguities, and simplifying language. A first evaluation highlights Chat-GPT's ability to deal with a classic archaic literary vocabulary, also raising issues on managing criteria for better grasping the meanings and forms and about the multicultural extent of content transfer.

Introduction
Consisting today in 3,782 icons, regularly updated by Unicode Consortium, URL the emoji international catalogue contains signs for \emoji{grinning-face} facial expressions, \emoji{person-shrugging} human gestures, \emoji{person-cartwheeling} people activities, \emoji{cook} jobs, \emoji{potted-plant} plants, \emoji{squid} animals, \emoji{spaghetti} food, \emoji{light-bulb} objects, \emoji{train} symbols of travel, \emoji{tokyo-tower} places, \emoji{rainbow-flag} flags, \emoji{input-numbers} numbers, and \emoji{red-square} geometrical forms. While the visual content seems to be able to provide an encyclopedic list with universal significance, ideally capable of conveying language-independent meanings, the interpretation of emojis is, on the contrary, highly arbitrary. They are strongly subject to ambiguities and variations due to linguistic, cultural, and personal specificities. The use of emoji has considerably increased over time, and besides complementing written texts in online messages and posts as a nice means to express feelings and emotional statuses, emojis are also used to completely replace verbal language statements AUTHOR. Experiments have been carried out to explore the feasibility of using emojis as language to convey meanings through emoji-only translations. Notable examples include the popular Emoji Dick project, the translation into emoji of Moby Dick AHUTOR, or Wonderland AHUTOR, an emoji poster created in 2014 to reproduce the full story of Lewis Carroll’s Alice in Wonderland. These earliest experiments, however, lack codification and, as such, cannot be considered as a language, that is, a shared system in the Saussurean sense AHUTOR. The first translation, in fact, was crowdsourced in a free and creative way, while the second one was an individual and literal translation experiment from English. A concrete attempt to create a truly codified emoji language can be represented by Emojitaliano AHUTOR. Emojitaliano is an emoji code originated from a crowdsourcing experiment initiated by a social community, specifically created to share a common emoji language able to counteract the natural polysemy of emojis. Born with the translation of Collodi’s Pinocchio, The Story of a Puppet AHUTOR, the structure and glossary of Emojitaliano have been afterwards usefully reapplied for the translation of texts of different genres such as the technical declaratory prose of the Italian Constitution , the Manifesto of non-hostile communication, the narrative prose of classic moral tales (i.e., The Wolf and the Lamb, in figure pecora, Giacomo Leopardi’s lyrical poem L’infinito. Emojitaliano is based on the assessment of conventional meanings and syntax, capable of guaranteeing the sharing of sense by means of intersemiotic translation, beyond subjective interpretations. Emojitaliano provides a grammatical structure and a shared vocabulary which can be expanded and re-shared with each new translation AHUTOR. Recent experiments have opened new research horizons in evaluating the capability of large language models (LLMs) to translate words or text into emojis. This is predicated on the assumption that, given LLMs are trained on extensive corpora sourced from the internet, they have been exposed to emojis and are able to grasp the semantics of emojis AHUTOR. Recently, Text2Emoji AHUTOR was proposed as an automatic translator, based on a large text-emoji parallel corpus, created by prompting the LLM, Chat-GPT (OpenAI, 2023) and EmojiLM, a sequence-to-sequence model specialised in the text-emoji bidirectional translation. Another translation experiment involving emojis, conducted by AHUTOR is Emojinize. This experiment leverages the power of LLMs to translate text by considering both prior and subsequent contexts, which differs from next-token prediction. Emojinize disambiguates synonyms based on context, unlike a static lookup table, and harnesses the expressive power of combining multiple emojis. Among the experiments, a first attempt using Chat-GPT to learn the Emojitaliano grammar was also carried out in 2023 by the Emojitaliano research group. Assuming the fundamental role of a conventional syntax as a basis for each shared code AHUTOR, the aim was to verify the ability of LLMs to learn and reapply the Emojitaliano grammar rules to produce translations of Pinocchio on its own AHUTOR. In this paper we present a follow-up experiment of automatic translation into emoji, focused on special vocabulary. Chat-GPT's translations of an authorial lexicon have been tested and then compared to the corresponding human solutions.The purpose is to test LLMs capabilities in autonomously rendering complex vocabulary, in the horizon of building a translation tool into emoji as a means of language simplification: the general project and the conlang itself are named Emojilingo and available online on URL. The paper is organised as follows: section 2 introduces the Emojilingo project Parole di Dante, the subject being translations in emoji of 365 words (Parole di Dante) from Dante’s poem Divine Comedy. Section 3 presents the AI translation experiment carried out with two versions of Chat-GPT (3.5 and 4) AHUTOR on the 365 Dante's words, with a focus on the method and descriptions of some examples. Section 4 provides an evaluation of the results, also obtained through AI models and through a similarity matrix, and the closing section includes conclusions and ideas on future work.

Conclusion
In this paper we presented a translation experiment into emojis using two versions of Chat-GPT, to compare them with a human version, already available, realized within the framework of the Emojitaliano experience. The present project focuses on an integrated translation program, that combines both human (Emojitaliano) and automated approaches, as a basis for a constructed emoji-based pivot language: Emojilingo. Using a zero-shot prompting approach, both Chat-GPT versions (3.5 and 4) provided emoji translations for 365 words extracted from Dante's Comedy, along with explanations for the their own translation solutions. We also had Chat-GPT evaluate the three different translations produced within the Emojitaliano project, alongside those produced by Chat-GPT. The present experiment substantially succeeds in confirming AI easiness and ability to use emojis to convey linguistic meanings, also managing special and archaic vocabulary. We in fact tested the machine's ability to handle denotative and connotative issues in the different translation choices, i.e. the translation solutions can be multi-faceted, each one catching some of the many semantic features underlying words. In this sense most solutions may be acceptable, such as to demonstrate the versatility of the emoji code to convey the senses. Within this broad faculty of choice, however, some options seem quite critical, due to the dissimilarity of cultural values expressed by the languages, and by the emojis themselves. That is, a main consequence of using AI for translation, also in emojis, is the reaffirmation of the crucial challenge in international translation: the need for careful attention to specific cultural dimensions during localization AUTHOR. Cultural values underlie texts, words and languages, as, for example, a `pig' is an `occidental' symbol for negative concepts as `dirt' and `gluttony' (as in `lurco'), while the animal has a totemic or sacred value elsewhere; likewise, colors, or gestures, take on cultural values according to societies and cannot be accorded univocal international meanings. The choice of an icon as and international multilingual sign cannot override cultural peculiarities. Finally, cultural vocabularies may vary on the basis of literary contexts and textual genres, often conveying suggestions related to signifiers that are now lost. Given the conservative structure of poetical language, emoji translations may therefore need to move beyond the broadness of interlingua to fully convey meanings by reproducing linguistic signs `verbatim' (es. `intuarsi' \emoji{person-standing}\emoji{magic-wand}\emoji{backhand-index-pointing-up}): that is, the literal solution, usually ruled out from the perspective of an international semantic code, becomes substantial to recover the cultural dimension of a literary text AUTHOR. Special care is therefore required in selecting corresponding matches in emoji so that they do not conflict with reception in different countries and societies and so that they do succeed in reaching the core content of the original, which is the main purpose of `the emojilingua'. future research will always need a human evaluation of automated outcomes, carried on by a team with extensive expertise in cross-cultural perspectives, and with a deep understanding of cultural values of emojis. This will help to limit unrestricted creativity and ensure a wide common comprehension of Emojilingo, and its highest exportability.