@inproceedings{lee-etal-2020-postech,
title = "{POSTECH}-{ETRI}{'}s Submission to the {WMT}2020 {APE} Shared Task: Automatic Post-Editing with Cross-lingual Language Model",
author = "Lee, Jihyung and
Lee, WonKee and
Shin, Jaehun and
Jung, Baikjin and
Kim, Young-Kil and
Lee, Jong-Hyeok",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.82",
pages = "777--782",
abstract = "This paper describes POSTECH-ETRI{'}s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.",
}
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<abstract>This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.</abstract>
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%0 Conference Proceedings
%T POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model
%A Lee, Jihyung
%A Lee, WonKee
%A Shin, Jaehun
%A Jung, Baikjin
%A Kim, Young-Kil
%A Lee, Jong-Hyeok
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F lee-etal-2020-postech
%X This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.
%U https://aclanthology.org/2020.wmt-1.82
%P 777-782
Markdown (Informal)
[POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model](https://aclanthology.org/2020.wmt-1.82) (Lee et al., WMT 2020)
ACL