@inproceedings{aldarmaki-diab-2019-scalable,
    title = "Scalable Cross-Lingual Transfer of Neural Sentence Embeddings",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    editor = "Mihalcea, Rada  and
      Shutova, Ekaterina  and
      Ku, Lun-Wei  and
      Evang, Kilian  and
      Poria, Soujanya",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-1006/",
    doi = "10.18653/v1/S19-1006",
    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."
}