If Apple Translate is to improve, it better consider these parallel corpora tips

Apple Translate, like other machine translation systems, relies on a large amount of high-quality, parallel corpora for ML that is designed to train and improve its translation models. This data consists of the same text in two or more languages, aligned so that the system can learn correspondences between linguistic units (words, phrases, sentences) in the different languages. In today’s blog, we are going to that if Apple Translate is to improve, it better consider these parallel corpora tips.

Screenshot of Apple Translate in iOS — Screenshot of Apple Translate at work in iOS

General Domain Data: This forms the backbone of a translation system. It includes a vast collection of text from books, websites, and other publicly available resources. This type of data helps the system learn basic vocabulary, grammar rules, and common phrases in a variety of languages.
Specialized Domain Data: To enhance performance in specific areas such as legal, medical, or technical translation, Apple Translate would benefit from parallel data from these domains. Such data could include legal documents, medical research papers, and technical manuals, respectively.
Informal and Colloquial Language Data: To improve translations of informal and colloquial language, which is common in social media, chat messages, and some types of user-generated content, the system would need parallel data that includes this kind of language. This could come from social media posts, forum threads, and other similar sources.
Diverse Cultural Contexts Data: Language usage varies greatly across different cultures and regions, even for the same language. To enhance cultural and regional appropriateness, Apple Translate might need data that reflects these variations, such as texts from different countries where a language is spoken.
Customer Interaction Data: This could include anonymized and privacy-compliant data from users of Apple Translate. Analyzing mistakes and user corrections could provide valuable insights to improve the system.
Audiovisual Data: For improving spoken language translation or translation of multimedia content, Apple Translate might need parallel corpora that includes transcriptions and translations of audio or video content.
Quality-Controlled and Revised Translation Data: This would include translations that have been manually revised and corrected by professional translators. This type of high-quality data can provide a standard for the system to aspire to.

Collecting and incorporating these types of parallel corpora, while respecting user privacy and data protection regulations, could greatly enhance the breadth and depth of Apple Translate’s capabilities and help it compete with current large actors in online MT like Google Translate, Microsoft Bing Translator, DeepL, Yandex Translate or Sogou.

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