@inproceedings{liu-etal-2019-incorporating-word,
title = "Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring",
author = "Liu, Zihan and
Xu, Yan and
Winata, Genta Indra and
Fung, Pascale",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5327/",
doi = "10.18653/v1/W19-5327",
pages = "275--282",
abstract = "This paper describes CAiRE`s submission to the unsupervised machine translation track of the WMT`19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations."
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<abstract>This paper describes CAiRE‘s submission to the unsupervised machine translation track of the WMT‘19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.</abstract>
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%0 Conference Proceedings
%T Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
%A Liu, Zihan
%A Xu, Yan
%A Winata, Genta Indra
%A Fung, Pascale
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-incorporating-word
%X This paper describes CAiRE‘s submission to the unsupervised machine translation track of the WMT‘19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.
%R 10.18653/v1/W19-5327
%U https://aclanthology.org/W19-5327/
%U https://doi.org/10.18653/v1/W19-5327
%P 275-282
Markdown (Informal)
[Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring](https://aclanthology.org/W19-5327/) (Liu et al., WMT 2019)
- Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring (Liu et al., WMT 2019)
ACL
- Zihan Liu, Yan Xu, Genta Indra Winata, and Pascale Fung. 2019. Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 275–282, Florence, Italy. Association for Computational Linguistics.