Scalable Cross-Lingual Transfer of Neural Sentence Embeddings

Hanan Aldarmaki; Mona Diab

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arXiv · 1904.05542

Scalable Cross-Lingual Transfer of Neural Sentence Embeddings

Hanan Aldarmaki·Mona Diab

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Abstract

We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.

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Hanan Aldarmaki, Mona Diab. 2019-04-11. Scalable Cross-Lingual Transfer of Neural Sentence Embeddings. https://arxiv.org/abs/1904.05542

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