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Noroff Case Studies

Google Translate Case Study

Author: Silje Walmann <SiljeW>

Introduction

This written case study entails the Google Translate platform and its development before and after the very first launch.

Google Translate is based on Neural Machine Translation(NMT) that uses artificial neural networks to predict the likelihood of a sequence of words. The Neural Network has an encoder-decoder architecture with attention mechanisms to focus on relevant parts of the input. This tool has been and is still being used for many translation purposes, combining a high accuracy metric and numerous languages supported worldwide.

This research report dives deeper into how the technology was developed, how the main features work, and covers relevant weaknesses for further development.

Brief History

The very first Machine Translation system was developed more than 50 years before the first release of Google Translate, and has led to the more complex and accurate system that we have today. However, the system went through different methods for translation as listed below.

  • Timeline for Machine Translation Technologies:

    • 1950-1980: Rule Based Machine Translation (RBMT)
    • 1980: Example-Based Machine Translation (EBMT)
    • 1990-2015: Statistical Machine Translation (SMT)
    • 2015-now: Neural Machine Translation (NMT)

  • Timeline for Google Translate:

    • 2004: Google begins exploring machine translation, initially using SYSTRAN’s RBMT system for their first language pairs.
    • 2006: Google starts developing its own SMT system, moving away from SYSTRAN’s technology. Franz Josef Och, previously at DARPA, leads the development team.
    • 2007: Official launch of Google Translate as a free service, initially supporting translation between Arabic, Chinese, and English.
    • 2008-2009: Rapid expansion of language pairs and introduction of the first mobile version through a web app for smartphones.
    • 2010: Launch of Google Translate mobile app for Android, followed by iOS in 2011. Introduction of “Conversation Mode” for real-time speech translation.
    • 2014: Introduction of the Google Translate Community, allowing users to contribute translations and improvements.
    • 2015: Launch of Word Lens integration, enabling real-time visual translation of text through smartphone cameras.
    • 2016: Major shift to NMT system, significantly improving translation quality. Initially launched for eight language pairs.
    • 2017: Extension of NMT to almost all language pairs. Introduction of offline NMT for several languages.
    • 2018: Integration of Google Translate into various Google services including Chrome, Maps, and Assistant. Launch of real-time transcription features.
    • 2020: Major improvements to offline translation capabilities and enhanced camera translation features.
    • 2022: Introduction of Zero-Shot Machine Translation. “Transcribe” mode for real-time translation of continuous speech, and expansion to support over 100 languages.
    • 2023: Implementation of more advanced AI models to improve translation accuracy and context understanding, particularly for low-resource languages.
    • 2024:

Main Features

  • Unique Value Proposition:

    • Scale and Reliability: Massive training data with continuous improvements, robust infrastructure and high availability
    • Neural Machine Translation: Context-aware translations rather than word-by-word, better handling of idioms and cultural nuances, improved grammar in translated content and ability to learn from corrections and usage
    • Integration Capabilities: API access for web developers, built into Chrome browsers, mobile device damera integration and real-time conversation features

  • Problem solving features:

    • Language Barriers in communication:
      • Communication barriers for visual and hearing impairment (text to speech, speech to text)
      • Translation needs in international business
      • Emergency situations where quick communication is crucial
      • Tourist interactions in foreign countries
      • Educational settings with international students

  • Developer benefits:

    • Development Applications: Website localization automation, multi-language app development, content management systems integration and user interface translation
    • Use Cases for Developers:
      • Building multilingual applications
      • Creating language learning tools
      • Implementing chat applications with translation
      • Developing content management systems with translation support
    • API integration:
Feature CategoryDescriptionUse Cases
Text TranslationReal-time translation of written text between 100+ languages• Document translation • Website content • Social media posts • Email communication
Voice TranslationSpeech-to-speech translation in real-time• In-person conversations • Phone calls • Voice commands • Travel assistance
Camera TranslationReal-time translation of text through camera lens• Street signs • Menus • Product labels • Printed documents
Offline TranslationTranslation capability without internet connection• Travel in remote areas • Limited connectivity zones • Data-saving mode
Conversation ModeTwo-way real-time translation between speakers• Face-to-face conversations • Business meetings • Customer service • Educational settings
Handwriting InputTranslation of handwritten text• Written notes • Handwritten signs • Personal letters
Dictionary FeatureDetailed word definitions and examples• Language learning • Context understanding • Vocabulary building
Website TranslationAutomatic translation of entire web pages• Research • International browsing • Content localization
TranscriptionReal-time conversion of speech to text• Meeting notes • Lectures • Presentations • Audio content
Cross-platform SyncSynchronization across devices and platforms• Mobile devices • Desktop browsers • Tablets • Smart devices

Market Comparison

Feature/AspectGoogle TranslateDeepLMicrosoft TranslatorAmazon TranslateYandex.Translate
Core TechnologyNeural MT + BERTCustom Neural NetworkCustom Neural MTNeural MT + AWS AINeural MT
Languages Supported133+31100+75+100+
Accuracy Metrics*85-90%90-95%85-90%85-90%80-85%

Other features than mentioned above, such as key strengths, limitations and developer features, shows pros and cons of different use and implementations. For example, DeepL have higher accuracy and better context understanding, but is limited to less languages. However, the translation systems in general have several common weaknesses:

  • Context and Nuance:

    • Often misses cultural context and idioms
    • Struggles with double meanings and wordplay
    • Cannot reliably understand sarcasm or subtle humor
    • May miss professional or industry-specific terminology
    • Has difficulty maintaining consistent tone and formality level

  • Grammar and Structure:

    • Struggles with complex sentence structures
    • Can have issues with word order in languages with different syntactic structures
    • May not handle tenses correctly across languages

  • Language-Specific Issues:

    • Performance varies greatly between language pairs
    • Less accurate for languages with limited training data
    • Struggles with languages that have different writing systems
    • May have difficulty with dialects and regional variations

  • Document Format:

    • Can lose formatting when translating documents
    • May struggle with tables and specialized layouts
    • Sometimes misinterprets bullet points and numbered lists
    • Can have issues with technical or scientific notation

  • Reliability:

    • May give different translations for the same text at different times
    • No guaranteed accuracy for official or legal documents
    • Can propagate errors when texts are translated back and forth

  • Literary and Creative Content:

    • Poor handling of poetry and creative writing
    • Cannot maintain rhyme or meter
    • Loses metaphorical meanings
    • Struggles with cultural references

  • Specialized Content:

    • Less reliable for technical documentation
    • May mistranslate medical terminology
    • Legal text translations can be dangerously inaccurate
    • Scientific content may lose precise meanings

  • Gender and Pronouns:

    • Often makes assumptions about gender and gender roles
    • May not handle non-binary language appropriately
    • Can be inconsistent with pronoun usage
    • Struggles with languages that have different gender systems

Conclusion

The main advantages for Google Translate is that it’s free and easily accessible across multiple platforms, with real-time translation capabilities and integration with other Google services.

However, it has limited accuracy for complex or technical content as well as context and cultural nuances. This affects the general reliability of the tool, and translation results should be used with an understanding of these limitations.

It is a helpful and valuable tool in many circumstances, making communication and learning more convenient due to the many languages it supports. In order enhance Google Translate in the future, there should be a continued improvement through advanced AI models, with verification capabilities.

References

Additional Resources