Trains and serves large language models for machine translation, integrating n-gram and neural model layers for high-quality cross-lingual output. Foundational early-era LLM-in-translation work that prefigured the LLM-first translation systems now deployed industry-wide.
Patent Overview
- Inventor
- Jeffrey Dean, others
- Assignee
- Google LLC
- Filed
- 2007
- Granted
- 2012-12-11
The Challenge
The Challenge
Machine translation needs both lexical accuracy and fluency. Statistical phrase-based systems handle lexical matches well but produce stilted output. Large language models trained on massive monolingual corpora restore fluency, but integrating them with translation systems is non-trivial.
- Phrase-Based Translation Lacks Fluency — Statistical phrase-based systems map words and phrases accurately but produce target text that reads unnaturally.
- Large Language Models Provide Fluency — LLMs trained on massive target-language corpora capture natural phrasing patterns. Integration improves output fluency.
- Model Size Strains Serving Latency — Large models exceed naive serving latency budgets. Distributed serving and quantization required.
- Smoothing Handles Sparse N-Grams — Even large training corpora leave rare n-grams unobserved. Smoothing methods (Kneser-Ney, interpolation) handle the gaps.
- Translation And Language Models Must Combine Coherently — Translation model scores and language model scores must combine in a way that preserves both lexical accuracy and fluency.
Innovation
How The System Works
The system trains a large language model on massive target-language corpora, distributes the model across serving infrastructure, integrates language-model scores with translation-model scores during decoding, applies smoothing for rare n-grams, and outputs fluent translation candidates.
- Collect Training Corpora — Massive monolingual target-language corpora collected. Diverse genres and registers covered.
- Train Language Model — N-gram and neural language models trained on corpora. Large-N n-grams capture phrasing patterns.
- Apply Smoothing — Smoothing methods (Kneser-Ney, interpolation) handle rare or unseen n-grams.
- Distribute Model Serving — Large models partitioned across serving nodes. Distributed access supports query-time use.
- Integrate With Translation Model — Translation-model scores and language-model scores combine in decoder. Per-hypothesis score drives candidate selection.
- Decode Translations — Beam-search or comparable decoder produces translation candidates. Language model scores rank candidates by fluency.
- Continuous Retraining — New corpora and improved smoothing periodically retrain model. Translation quality improves over time.
Language Model Fluency
The patent's load-bearing idea is that large language models trained on monolingual target corpora restore fluency to translation output. Integration with translation models yields output that is both accurate and natural.
Fluency Lives In Language Models
Translation models capture lexical mapping; language models capture phrasing. Combining the two yields output that reads naturally in the target language.
- Massive Monolingual Training — Target-language corpora at massive scale capture natural phrasing patterns. Genre and register diversity matters.
- Distributed Serving — Large models partitioned across serving nodes. Distributed access fits within latency budgets.
- Score Integration — Language-model scores combine with translation-model scores during decoding. Per-hypothesis score balances accuracy and fluency.
Technical Foundation
Technical Foundation
The patent specifies the corpus collector, model trainer, smoother, distributed serving layer, decoder integration, and retraining loop.
- Corpus Collector — Massive monolingual target-language corpora collected across genres and registers.
- Model Trainer — N-gram and neural language models trained on corpora. Large-N captures phrasing patterns.
- Smoother — Kneser-Ney, interpolation, or comparable smoothing handles rare or unseen n-grams.
- Distributed Serving Layer — Models partitioned across serving nodes. Query-time distributed access supports latency budget.
- Decoder Integration — Translation-model and language-model scores combine in decoder. Per-hypothesis scoring drives candidate selection.
- Retraining Loop — Periodic retraining incorporates new corpora and improved methods. Translation quality compounds.
The Process
The Process
Training runs offline; serving runs distributed; decoding runs per translation query. Periodic retraining keeps quality current.
- Collect Corpora — Massive target-language monolingual corpora aggregated.
- Train Model — Language model trained on corpora. Smoothing applied for rare n-grams.
- Partition For Serving — Model partitioned across serving nodes.
- Receive Translation Query — Source-language text arrives.
- Decode With Combined Scoring — Decoder integrates translation-model and language-model scores.
- Return Top Candidate — Highest-scoring hypothesis returned as translation.
- Periodic Retraining — New corpora and methods periodically retrain model.
Quality Control
Quality Control
Translation quality depends on training data, smoothing, and integration correctness. The patent specifies safeguards.
- Corpus Quality Filtering — Training corpora filtered for quality. Low-quality or off-genre text excluded.
- Smoothing Calibration — Smoothing parameters calibrated against held-out data. Wrong calibration degrades rare-n-gram handling.
- Distributed-Serving Consistency — Model partitions must remain consistent across serving nodes. Inconsistency produces query-dependent quality variation.
- Decoder Beam-Size Tuning — Beam size trades latency against quality. Per-language tuning optimizes the trade-off.
- Continuous Evaluation — Per-language quality metrics (BLEU, human eval) tracked. Regressions trigger investigation.
Real-World Application
Large-language-model integration in translation prefigured the modern LLM-first translation era. The primitives (massive monolingual training, distributed serving, integrated decoding) underpin every modern translation system.
- Massive monolingual Training Data — Target-language corpora at scale. Genre and register diversity capture phrasing patterns.
- Distributed Serving Architecture — Large models partitioned across nodes. Query-time distributed access fits latency budget.
- Integrated decoding Quality Method — Translation and language model scores combine. Balances accuracy and fluency in candidate ranking.
Why Cross-Lingual Content Quality Matters
Modern translation systems use large language models. Source-language content that is well-written, clear, and structurally clean translates more accurately. Translation quality compounds across publishing in many languages.
Why Quality Source Beats Quality Translation Engine
Even the best translation system struggles with ambiguous, jargon-heavy, or poorly structured source text. Investing in source-language clarity pays compound dividends across every translated locale.
<\/section>What This Means for SEO
What This Means for SEO
This patent integrates large language models with phrase-based translation to restore fluency, using massive monolingual training and distributed serving. SEO implication: translation quality is now LLM-driven, and clean, well-structured source content translates far more accurately, so source clarity compounds across every locale you publish in.
- Source Clarity Compounds Across Locales — Even the best translation system struggles with ambiguous or jargon-heavy source text. Investing in clear, well-structured source-language content pays compound dividends across every translated version.
- Structure Survives Translation Better — Well-organized, clean prose translates more accurately than tangled or fragment-heavy writing. Tight sentence structure in the source language reduces translation errors downstream.
- Fluency Comes From Language Models — LLMs trained on huge monolingual corpora supply natural target-language phrasing on top of lexical accuracy. Modern translated content reads naturally, so thin machine-translated pages no longer pass as obviously low quality on fluency alone.
- Avoid Ambiguity And Heavy Jargon — Ambiguous phrasing and dense jargon degrade translation quality. Writing for clarity in the source language directly improves how well your content serves international audiences.
- Multilingual Publishing Scales On Good Source — Because the engine compounds quality across languages, one well-written source page yields better results across many locales. Prioritize source quality before scaling translation volume.
- Translation Is Not A Substitute For Localization — The system optimizes fluency, but accuracy still depends on the source being clear and structurally clean. Clean source plus genuine localization beats raw machine output.
- Quality Source Beats A Better Engine — Improvements in the translation engine cannot rescue poor source text. The highest-leverage investment is the original content, not the translation step.