How can machine translation systems be developed for non-Latin languages?

1 Script and orthography

One of the first challenges of MT for non-Latin languages is to deal with the script and orthography of the source and target languages. Script refers to the system of symbols used to write a language, such as Latin, Cyrillic, Arabic, or Chinese. Orthography refers to the rules and conventions of spelling, punctuation, and capitalization. Different scripts and orthographies can affect the readability, segmentation, and alignment of texts, as well as the encoding and storage of data. For example, some scripts, such as Arabic or Hebrew, are written from right to left, while others, such as Chinese or Japanese, are written in vertical columns. Some scripts, such as Devanagari or Thai, do not mark word boundaries with spaces, while others, such as Chinese or Japanese, use logographic characters that represent words or concepts rather than sounds. Some scripts, such as Arabic or Persian, have complex rules of diacritics and ligatures that modify the shape and pronunciation of letters. To overcome these challenges, MT systems need to use appropriate tools and methods to convert, normalize, tokenize, and align texts in different scripts and orthographies.

2 Morphology and syntax

Another challenge of MT for non-Latin languages is to cope with the morphology and syntax of the source and target languages. Morphology refers to the structure and formation of words, such as roots, stems, affixes, and inflections. Syntax refers to the structure and formation of sentences, such as word order, agreement, case, and tense. Different languages have different levels of morphological and syntactic complexity and diversity, which can affect the accuracy and fluency of MT systems. For example, some languages, such as Turkish or Finnish, have rich and productive morphology, which means that they can form many words from a single root by adding various suffixes. Some languages, such as Arabic or Hindi, have complex and variable word order, which means that they can place words in different positions in a sentence depending on the context and emphasis. Some languages, such as Russian or German, have elaborate and inflected case systems, which means that they can mark the role and relation of words in a sentence by changing their endings. To cope with these challenges, MT systems need to use sophisticated models and algorithms to analyze, generate, and translate words and sentences in different languages.

3 Lexicon and semantics

A third challenge of MT for non-Latin languages is to handle the lexicon and semantics of the source and target languages. Lexicon refers to the vocabulary and dictionary of a language, such as words, meanings, synonyms, and antonyms. Semantics refers to the meaning and interpretation of words and sentences, such as sense, reference, ambiguity, and pragmatics. Different languages have different lexical and semantic features and variations, which can affect the adequacy and naturalness of MT systems. For example, some languages, such as Chinese or Korean, have many homographs and homophones, which means that they have words that share the same spelling or sound but have different meanings. Some languages, such as Arabic or French, have many idioms and expressions, which means that they have words or phrases that have figurative or cultural meanings that cannot be translated literally. Some languages, such as English or Spanish, have many loanwords and calques, which means that they have words or phrases that are borrowed or adapted from other languages. To handle these challenges, MT systems need to use extensive and reliable resources and methods to identify, disambiguate, and translate words and sentences in different languages.

4 Evaluation and quality

A final challenge of MT for non-Latin languages is to evaluate and improve the quality of the output. Quality refers to the degree of satisfaction and usefulness of the MT system for the intended purpose and audience. Quality can be measured by various criteria and methods, such as accuracy, fluency, adequacy, naturalness, readability, and usability. Different languages and domains have different quality expectations and standards, which can affect the performance and feedback of MT systems. For example, some languages, such as Japanese or Arabic, have high linguistic and cultural sensitivity, which means that they require careful and respectful translation of texts and contexts. Some domains, such as legal or medical, have high accuracy and reliability requirements, which means that they demand precise and consistent translation of terms and concepts. Some purposes and audiences, such as information or entertainment, have high fluency and naturalness preferences, which means that they prefer smooth and idiomatic translation of texts and styles. To evaluate and improve the quality of MT for non-Latin languages, MT systems need to use various data and techniques to test, compare, and optimize the output.