Scalable Cross-Lingual Transfer of Neural Sentence Embeddings [article]

Hanan Aldarmaki, Mona Diab
<span title="2019-04-11">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... ence 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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