Machine Translation

Machine translation (MT) is an interdisciplinary scientific field that brings together linguists, lexicologists, computer scientists, and translation practitioners in the pursuit of a common goal: to design and develop electronic resources and computer software capable of automatically translating a document in a source language (SL) into an equivalent text in a target language (TL). By extension, machine translation technologies also include tools aimed at helping human translators to perform their work more efficiently using computer-assisted translation (CAT) technology. Machine translation started in the late 1950s with attempts to automatically translate Russian into English. Realization of the extreme difficulty of the task led the MT community to concentrate its efforts on more focused and realistic problems, starting the field of natural language processing (NLP) studies. MT was thus broken down into three main sub-issues: analyzing the SL into a more abstract representation, transferring this representation into an equivalent target representation, and, finally, generating a proper surface realization in TL. Capitalizing on the progress in applied NLP and artificial intelligence, MT made slow progress over the next thirty years, using mostly symbolic models of language processing to accomplish the analysis, transfer, and generation processes. Despite of several remarkable achievements, these models were challenged in the 1980s by corpus-based methodologies, which rely on the analysis of large bodies of manually translated bitexts to generate translations of new documents. In particular, the statistical approaches in machine translation introduced in the early 1990s, and subsequently improved during the next decade, have rapidly gained momentum. As of 2014, statistical approaches have been superseded by more powerful machine learning techniques based on artificial neural networks. Relying on the systematic exploitation of huge corpora of monolingual texts and multilingual bitexts available on the Internet, ” Neural Machine Translation” appears to be the most effective approach today for a wide variety of uses. Neural approaches can handle almost any language pair, provided a sufficient access to parallel corpora is available. A remarkable recent evolution is the development of multilingual translation models that are able to handle multiple languages directions in one single system.