BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
ICML(2014)
摘要
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple
and computationally-efficient model for learning bilingual distributed
representations of words which can scale to large monolingual datasets and does
not require word-aligned parallel training data. Instead it trains directly on
monolingual data and extracts a bilingual signal from a smaller set of raw-text
sentence-aligned data. This is achieved using a novel sampled bag-of-words
cross-lingual objective, which is used to regularize two noise-contrastive
language models for efficient cross-lingual feature learning. We show that
bilingual embeddings learned using the proposed model outperform
state-of-the-art methods on a cross-lingual document classification task as
well as a lexical translation task on WMT11 data.
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