Facebook is one of the largest and most popular social media platforms in the world, with over 2.9 billion monthly active users as of the fourth quarter of 2021. With users distributed across the globe speaking dozens of languages, providing accurate translations is crucial for Facebook to connect people worldwide.
Opening Summary
Facebook uses its own in-house translation systems to translate content on Facebook and Instagram into over 100 supported languages. The main translation services Facebook relies on are:
- Facebook’s Neural Machine Translation (NMT) system, based on artificial neural networks.
- Facebook’s Translation Memory system, which utilizes past translations to improve quality.
- Facebook’s Beam Search decoding algorithm, which generates high-quality translations.
In 2016, Facebook transitioned to neural machine translation, which reduced translation errors by an average of 60% compared to the prior phrase-based statistical machine translation system. Facebook continues to improve its systems, now translating over 2 billion pieces of content daily with quality approaching human-level.
How Facebook Performed Translations Historically
In the early days of Facebook, launched in 2004, the platform relied fully on users to translate content manually. There was no automated translation system in place.
As Facebook expanded internationally in the late 2000s and the number of supported languages grew, reliance on user translations became unsustainable. Facebook needed to develop in-house translation capabilities to bridge language barriers.
In 2012, Facebook transitioned to using a phrase-based statistical machine translation (SMT) system called Promt. This system generated translations by looking at patterns in massive bilingual text corpora in each language pair.
While a major improvement over purely manual translation, Promt had significant shortcomings. It frequently made grammatical mistakes and had limited context handling abilities. Meaning errors were also commonplace.
Issues with Statistical Machine Translation
Some key issues with Facebook’s original statistical phrase-based translation system included:
- Lack of fluency – translations were often disfluent and lacked natural language flow.
- Context ignorance – the system did not consider broader context, losing meaning.
- Training data reliance – low quality or unavailable training data resulted in poor translations between many language pairs.
- Ambiguity handling – the system could not effectively handle ambiguities in source languages.
Due to these issues, Facebook realized a new translation approach was needed to provide higher quality translations to its global user base. This led them to develop neural machine translation.
Transition to Neural Machine Translation
In 2016, after years of research and development, Facebook transitioned its translation services from phrase-based statistical models to neural machine translation (NMT).
Neural machine translation takes a different approach to automated translation than phrase-based SMT. Rather than simply looking at statistical patterns, NMT uses deep neural networks to understand full sentence contexts and their meanings.
The advantages of neural machine translation include:
- Fluency – NMT produces much more fluent and natural sounding translations.
- Context handling – neural networks can take broader context into account.
- Improved disambiguation – NMT is better at disambiguating words and phrases.
- Meaning preservation – meaning and intent are more likely to be preserved.
For Facebook, switching to NMT reduced translation errors by an average of 60% compared to the prior phrase-based SMT system. It also unlocked more reliable translation for low-resource languages lacking huge bilingual corpora.
How Facebook’s Neural MT System Works
Facebook uses an attentional sequence-to-sequence neural network architecture for machine translation. This consists of an encoder, decoder, and attention mechanism.
The encoder maps the source language sentence into a fixed-length vector representation. The decoder then generates the translation from the encoded vector. The attention mechanism gives context as the decoder translates each word.
Key advantages of this NMT architecture include:
- Variable length input and output handling via RNN encoder/decoders.
- Capture of dependencies and context through the encoder.
- Attention allows concentrating on relevant context at each step.
- Beam search decoding produces high quality output translations.
Facebook leverages massive datasets, strong computing power, and optimization techniques to train high-performance NMT models. They continue to improve model architecture and training methodology to enhance translation accuracy.
Integration with Translation Memory
In addition to neural machine translation, Facebook also integrates translation memory capabilities to further boost translation quality and consistency.
Translation memory stores previously translated content, allowing human translators and machines to leverage existing high-quality translations as a starting point. This provides a few key advantages:
- Improved fluency from relying on human-translated content.
- Consistency across similar content translated multiple times.
- Faster turnaround by reducing repetition.
- Cost savings from reduced human translation needs.
Facebook extracts translations from various sources into its translation memory, including:
- Human translations of Facebook products and content.
- Human translations from contracted translation vendors.
- High-quality existing translations of other web content.
The translation memory stores original source content alongside human translations. When new content matches something already in memory, automated systems can pull the original high-quality human translation. This provides a starting point, which machines then adapt to fit the new context.
Combined with neural translation capabilities, translation memory allows delivering fast, accurate, fluent translations across Facebook’s products and services.
The Scale of Facebook’s Translation Operations
The sheer size of Facebook’s global user base poses immense technical challenges for translation. With billions of users spanning thousands of language varieties, translation needs to handle massive data volumes at high accuracy.
Some key facts about the scale of Facebook’s translation operations include:
- 2.9+ billion monthly active Facebook users worldwide.
- 100+ languages supported on Facebook and Instagram.
- Over 500 million translations served daily across Facebook apps.
- 200+ countries with active Facebook users.
- 50+ locations worldwide with Facebook translation operations teams.
To handle this scale, Facebook relies on a combination of human translators, contractors, community reviewers, massive computing power, and optimized automated systems. AI advances in fields like NMT continue pushing the envelope on language coverage and quality.
Translation Infrastructure
Powering translation across billions of users and petabytes of data requires extensive infrastructure. Key elements include:
- Data centers with hundreds of thousands of servers to handle compute demands.
- High speed internal networks to transfer data between data centers.
- Hierarchical distributed caching to serve translations from memory efficiently.
- Load balancing and horizontal scaling to distribute workload evenly.
- Low latency design for fast response times to users worldwide.
Facebook has invested heavily in infrastructure to make real-time high-quality translation possible. Optimizations like caching and scaling allow serving thousands of translations per second with average latency of 50 milliseconds.
Continuous Improvement of Translation Systems
Translation technology continues advancing rapidly, with neural networks getting better each year. Facebook pours tremendous resources into further improving translation quality and scope.
Some key focus areas for continuous improvement include:
- More powerful NMT architectures such as Transformers, which widen context handling.
- Self-learning systems that learn from feedback and past mistakes.
- Boosting performance for low resource languages through transfer learning.
- Improving multilingual representations to enhance meaning transfer.
- Scaling systems to handle growing volumes of translation needs.
Facebook collaborates closely with top universities on translation research and runs internal workshops to share ideas. Regular production improvements ship, with translation error rates dropping steadily over time.
Facebook will continue leveraging the latest research and technology to bring higher quality translations to billions worldwide. The need for innovation remains strong as new languages and diverse content types pose fresh challenges.
Community Feedback and Review
In addition to improving automated systems, Facebook also relies on community feedback and review to enhance translation quality.
Some ways Facebook incorporates user input include:
- In-product feedback buttons to flag poor translations.
- Rating tools for users to score a translation’s quality.
- Community reviews of suggestions for alternate translations.
- Bug reporting channels to identify systemic issues.
Large groups of volunteer reviewers evaluate alternate translations and provide assessments. Native speakers are recruited across many language communities.
Feedback flows back into Facebook’s systems to correct errors, re-evaluate choices, and continue improving. Community input complements the technical advances powering Facebook’s translation engines.
Partnerships with Academic Institutions
Facebook works extensively with leading academic institutions to support translation research and develop new technologies.
Some key partnerships include:
- The University of Montreal – Collaborative NMT research and open source toolkit.
- New York University – Projects on multilingual NMT, transfer learning, and other areas.
- The University of Edinburgh – Research on low resource languages, multilingual models, and more.
Facebook established the Partnership on AI consortium in 2016 along with other technology leaders. This consortium funds AI safety research programs with several universities.
Internally, visiting researcher programs bring academic experts into Facebook for periods to collaborate on projects. External grants also support graduate students conducting research highly relevant to Facebook.
Through these partnerships and initiatives, Facebook aims to move translation technology forward across the entire field.
Open Source Contributions
Facebook is committed to sharing translation research with the broader community through open source projects.
Key open source initiatives related to translation include:
- PyTorch – Popular deep learning framework used to build Facebook’s NMT models.
- Fairseq – Sequence modeling toolkit focused on NMT models.
- Transformer – Attention model architecture that improves neural translation.
- Laser – Multilingual sentence embeddings to improve meaning transfer.
Facebook also co-created the FLORES evaluation benchmark for low resource languages. The WMT conference series features extensive contributions from Facebook’s translation teams.
Releasing innovations as open source benefits the entire AI community. It spurs new advances building upon Facebook’s work to unlock better automated translation worldwide.
Development of Internal Tools
Supporting Facebook’s massive translation operations requires extensive specialized internal tooling.
Some examples of custom tools include:
- aligned_comments & radius_ Corpora
tools to extract parallel sentences from public comments. - FMLN – Multilingual data sorting and cleaning.
- Orbit – Computer assisted translation tool.
- Ax – Platform for managing language assets like glossaries.
- Helium – Internal platform for launching NMT models.
These tools streamline workflows for linguists, computer scientists, and translators collaborating to continuously enhance translation quality for billions of Facebook users.
Custom development will remain important as new use cases emerge across Facebook’s global products and services. The unique scale of Facebook’s needs necessitates tailored solutions.
Translation Infrastructure Summary Table
Here is a summary of key facts about Facebook’s translation infrastructure and operations:
| Metric | Volume |
|---|---|
| Supported Languages | 110+ |
| Daily Translations Served | 500+ million |
| Monthly Active Users | 2.9+ billion |
| Servers for Translation | Hundreds of thousands |
| Translator Workforce | Thousands worldwide |
| Open Source Translation Projects | Dozens |
Conclusion
In summary, Facebook relies on a sophisticated blend of human translators, custom infrastructure, neural machine translation, translation memory, community feedback, academic partnerships, and continuous R&D to power translation across its global platforms.
Translation technology at Facebook has evolved greatly since the early days of purely manual crowdsourced translation. Today, neural networks translate billions of sentences daily at ever-improving quality levels.
While automatic translation continues advancing rapidly, human oversight remains essential to catching nuances and correcting mistakes. Facebook’s focus on combining human and machine abilities at global scale has enabled new levels of connection across languages.
Looking ahead, Facebook will continue leveraging cutting-edge research, open collaboration, and feedback from its international user community to bring the world closer together through better language translation.