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Ensemble Self-Training for Unsupervised Machine Translation

Ido Aharon, Jonathan Shaki, Sarit Kraus · Mar 17, 2026 · Citations: 0

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Mar 17, 2026, 7:16 PM

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Mar 17, 2026, 7:16 PM

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Abstract

We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT). Starting from a primary language pair, we train multiple UNMT models that share the same translation task but differ in an auxiliary language, inducing structured diversity across models. We then generate pseudo-translations for the primary pair using token-level ensemble decoding, averaging model predictions in both directions. These ensemble outputs are used as synthetic parallel data to further train each model, allowing the models to improve via shared supervision. At deployment time, we select a single model by validation performance, preserving single-model inference cost. Experiments show statistically significant improvements over single-model UNMT baselines, with mean gains of 1.7 chrF when translating from English and 0.67 chrF when translating into English.

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

Evaluation Modes

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

Quality Controls

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

Benchmarks / Datasets

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

Reported Metrics

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

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Evidence snippet: We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

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Research Brief

Deterministic synthesis

We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).

Generated Mar 17, 2026, 7:16 PM · Grounded in abstract + metadata only

Key Takeaways

  • We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT).
  • Starting from a primary language pair, we train multiple UNMT models that share the same translation task but differ in an auxiliary language, inducing structured diversity across models.
  • We then generate pseudo-translations for the primary pair using token-level ensemble decoding, averaging model predictions in both directions.

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