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arXiv · 1901.01577

Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes

Yunsu Kim·Julian Schamper·Hermann Ney
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Abstract

We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.

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Yunsu Kim, Julian Schamper, Hermann Ney. 2019-01-06. Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes. https://arxiv.org/abs/1901.01577

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