@InProceedings{aldarmaki-diab:2019:S19-1,
  author    = {Aldarmaki, Hanan  and  Diab, Mona},
  title     = {Scalable Cross-Lingual Transfer of Neural Sentence Embeddings},
  booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {51--60},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-1006}
}