@inproceedings{kim-etal-2019-learning,
title = "Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron",
author = "Kim, Yunsu and
Rosendahl, Hendrik and
Rossenbach, Nick and
Rosendahl, Jan and
Khadivi, Shahram and
Ney, Hermann",
editor = "Augenstein, Isabelle and
Gella, Spandana and
Ruder, Sebastian and
Kann, Katharina and
Can, Burcu and
Welbl, Johannes and
Conneau, Alexis and
Ren, Xiang and
Rei, Marek",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4309",
doi = "10.18653/v1/W19-4309",
pages = "61--71",
abstract = "We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine translation to force the source and target sentence embeddings to share the same space without the help of a pivot language or an additional transformation. We train a multilayer perceptron on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel or noisy parallel data. Our approach shows promising performance on sentence alignment recovery and the WMT 2018 parallel corpus filtering tasks with only a single model.",
}
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%0 Conference Proceedings
%T Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
%A Kim, Yunsu
%A Rosendahl, Hendrik
%A Rossenbach, Nick
%A Rosendahl, Jan
%A Khadivi, Shahram
%A Ney, Hermann
%Y Augenstein, Isabelle
%Y Gella, Spandana
%Y Ruder, Sebastian
%Y Kann, Katharina
%Y Can, Burcu
%Y Welbl, Johannes
%Y Conneau, Alexis
%Y Ren, Xiang
%Y Rei, Marek
%S Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kim-etal-2019-learning
%X We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine translation to force the source and target sentence embeddings to share the same space without the help of a pivot language or an additional transformation. We train a multilayer perceptron on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel or noisy parallel data. Our approach shows promising performance on sentence alignment recovery and the WMT 2018 parallel corpus filtering tasks with only a single model.
%R 10.18653/v1/W19-4309
%U https://aclanthology.org/W19-4309
%U https://doi.org/10.18653/v1/W19-4309
%P 61-71
Markdown (Informal)
[Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron](https://aclanthology.org/W19-4309) (Kim et al., RepL4NLP 2019)
ACL