Generates training data for language identification models by mining multilingual corpora for known-language samples, scaling language ID training without manual labeling and supporting hundreds of languages with minimal human effort.
Patent Overview
- Inventor
- Marc Najork, others
- Assignee
- Google LLC
- Filed
- 2013-06-28
- Granted
- 2015-01-01 (published application)
- Application Number
- US 13/930,816
The Challenge
The Challenge
Language identification models need labeled training data for every language they cover. Hand-labeling samples across hundreds of languages, including rare and low-resource languages, is prohibitively expensive. The system needs to mine existing multilingual corpora for known-language samples and use them as training data.
- Many Languages, Little Labeled Data — Major languages have abundant training data; long-tail languages (regional dialects, minority languages) have almost none. Hand-labeling does not scale to hundreds of languages.
- Existing Corpora Contain Language Signal — Wikipedia articles tag their language; Bible translations identify their target language; subtitle files declare their language. Existing corpora carry labels the system can harvest.
- Mined Labels Need Quality Validation — Some corpus labels are wrong: mistranslations, mixed-language entries, mislabeled subtitles. Mining must filter for label accuracy before training.
- Coverage Must Be Balanced — Mining produces uneven coverage across languages: English over-represented, regional languages under-represented. Training requires balancing for model quality across languages.
- Models Must Generalize Beyond Source Domain — Wikipedia text has style different from social media. Models trained only on encyclopedic text fail on conversational text. Cross-domain training is required.
Innovation
How The System Works
The patent mines multilingual corpora (Wikipedia, parallel texts, translations) for samples tagged with known language labels, validates label quality, balances coverage across languages, and produces training data for language identifier models without manual labeling.
- Identify Label-Bearing Corpora — Find corpora with explicit language labels: Wikipedia (article language tags), Bible translations, parallel-text corpora, multilingual news. Each corpus has its own label-extraction logic.
- Extract Samples With Labels — From each corpus, extract text samples with their associated language labels. Samples vary in size from sentences to paragraphs.
- Validate Label Accuracy — Per sample, validate the label using cross-checks: heuristic language detection, character-set analysis, frequency of language-specific markers. Validated samples enter training; failures are excluded.
- Balance Coverage Across Languages — Aggregate per-language sample counts. Apply balancing: cap over-represented languages, oversample under-represented ones with augmentation. Training distribution balances.
- Augment For Domain Diversity — Generate or include samples from diverse genres: encyclopedic, social media, technical, conversational. Models trained on diverse text generalize better.
- Train Language ID Model — Standard language-identification model trains on the mined labeled data. Per-language accuracy is monitored.
- Iterate As Corpora Update — As source corpora grow and new corpora emerge, mining re-runs. Language ID models retrain on expanded data.
Mining Replaces Hand-Labeling
The patent's load-bearing idea is that hand-labeling for language ID is unnecessary when multilingual corpora already carry language labels. Mining extracts the labels; validation cleans them; balanced training produces high-quality models without per-language labeling effort.
Labels Exist In Source Corpora
Wikipedia, parallel texts, subtitle files all carry explicit language labels. Reading these as training labels eliminates the labeling bottleneck for hundreds of languages.
- Multi-Corpus Mining — Multiple source corpora produce diverse labeled samples. Wikipedia, parallel texts, news, subtitle files each contribute.
- Label Validation — Mined labels validate via cross-checks. Wrong labels are caught and excluded before training.
- Coverage Balancing — Per-language sample counts balance via capping and augmentation. Training distribution prevents over-fitting to major languages.
Technical Foundation
Technical Foundation
The patent specifies the corpus catalog, per-corpus extractors, the label validator, the coverage balancer, the augmentation engine, and the training pipeline.
- Corpus Catalog — Per source corpus, configuration specifies how to access content and extract language labels. Catalog is extensible for new corpora.
- Per-Corpus Extractors — Each corpus has its own extractor that pulls text-label pairs in canonical format. Output is per-corpus labeled-sample stream.
- Label Validator — Per sample, validates label using heuristics, character-set analysis, and language-specific markers. Validation gate filters before training.
- Coverage Balancer — Aggregates per-language counts, applies caps to over-represented languages, oversamples under-represented ones via duplication or augmentation.
- Augmentation Engine — Generates synthetic samples for under-resourced languages or under-represented domains. Augmentation expands training-data diversity.
- Training Pipeline — Standard language-ID model architecture trains on the balanced labeled data. Per-language accuracy is monitored for deployment decisions.
The Process
The Process
The mining pipeline runs as a periodic batch. Output is a labeled training set that feeds language ID model training. Model deployments happen when accuracy targets are met.
- Configure Source Corpora — Per corpus, configuration specifies access and extraction logic. New corpora can be added without rebuilding the pipeline.
- Extract Labeled Samples — Per corpus, extractors pull text-label pairs in batch. Output streams to validation.
- Validate Labels — Per sample, validator runs cross-checks. Invalid samples drop; valid ones proceed.
- Aggregate And Balance — Per-language sample counts aggregate. Balancer applies caps and augmentation to produce balanced training set.
- Augment Domain Diversity — Augmentation engine generates samples for under-represented domains. Training data covers diverse genres.
- Train Model — Language-ID model trains on balanced data. Per-language accuracy is measured against held-out evaluation.
- Deploy When Accurate — Models meeting accuracy targets deploy to production. Refreshing happens as new training data accumulates.
Quality Control
Quality Control
Wrong labels propagate to wrong models. The patent specifies safeguards.
- Label Validator Calibration — Validators are calibrated against gold-standard labeled data. Wrong validators let bad labels through; calibration is essential.
- Per-Corpus Quality Audit — Per source corpus, label accuracy is audited periodically. Corpora with degrading label quality lose weight in training.
- Cross-Validator Agreement — Multiple validators run in parallel. Disagreement triggers manual review of edge cases.
- Per-Language Coverage Audit — Per language, sample count and diversity audit. Under-represented languages flag for additional corpus sourcing.
- Held-Out Accuracy — Per training cycle, held-out per-language accuracy measures. Below-threshold languages do not deploy with the model.
Real-World Application
Mined-data language ID underpins Google's broad language coverage across products: Translate, Search across 100+ languages, voice assistant multilingual support. The primitives generalize to any classification task where labels exist in source data.
- Hundreds Languages Supported — Mining scales to hundreds of languages. Hand-labeling could never achieve this coverage.
- Multi-corpus Source Diversity — Multiple corpora provide diverse training samples. Cross-domain training generalizes to many text types.
- Balanced Training Distribution — Per-language balancing produces models accurate across the language spectrum, not just for majority languages.
Why Multi-Language Content Benefits Globally
Content produced in multiple languages contributes to the cross-lingual training pool. Sites publishing in regional or under-represented languages can earn visibility in language-ID systems that mining helps cover.
Why The Mined-Data Pattern Generalizes
The mining-validation-balancing-training pattern works for any classification task where labels exist in source data: topic classification from Wikipedia categories, entity types from structured data, sentiment from labeled review corpora. The patent's primitives are reusable.
<\/section>What This Means for SEO
What This Means for SEO
This patent auto-generates language-identification training data by mining labeled multilingual corpora, validating labels, and balancing coverage across hundreds of languages. SEO implication: Google reliably identifies content language across the long tail, so accurate language signals and publishing in under-served languages can earn visibility.
- Language Is Detected Reliably At Scale — Mining scales language identification to hundreds of languages, including the long tail. The system accurately knows what language your content is in, so do not rely on language ambiguity.
- Under-Served Languages Are An Opportunity — Coverage balancing means even regional and minority languages get accurate models. Publishing quality content in under-represented languages can earn visibility where competition is thinner.
- Mixed-Language Pages Risk Misclassification — Validation filters mixed-language and mislabeled samples, and detectors read actual language markers. Pages mixing languages confuse identification, so keep each page in one clear language.
- Declare Language Accurately — The system cross-checks declared labels against character sets and language markers. Accurate language declaration in your markup aligns with what detection finds, reducing mismatch risk.
- Cross-Domain Text Generalizes — Models train on diverse genres so they handle conversational, technical, and encyclopedic text alike. Your content's style does not have to be formal to be correctly identified.
- Genuine Native Content Reads As Native — Detection relies on real language-specific markers and frequencies. Authentically written native-language content is identified cleanly, whereas thin machine output may carry telltale distributional patterns.
- Labels Already Exist In Source Data — The mining-and-validation pattern shows Google harvesting structure that already exists in corpora. The broader lesson is that genuine, well-structured signals you publish are readily picked up and reused.