@inproceedings{takahashi-etal-2020-automatic,
title = "Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model",
author = "Takahashi, Kosuke and
Sudoh, Katsuhito and
Nakamura, Satoshi",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.327/",
doi = "10.18653/v1/2020.acl-main.327",
pages = "3553--3558",
abstract = "We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references. The proposed method evaluates a translation hypothesis in a regression model. The model takes the paired source, reference, and hypothesis sentence all together as an input. A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors. Our experiments show that our proposed method using Cross-lingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences. Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance."
}
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<abstract>We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references. The proposed method evaluates a translation hypothesis in a regression model. The model takes the paired source, reference, and hypothesis sentence all together as an input. A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors. Our experiments show that our proposed method using Cross-lingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences. Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.</abstract>
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%0 Conference Proceedings
%T Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model
%A Takahashi, Kosuke
%A Sudoh, Katsuhito
%A Nakamura, Satoshi
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F takahashi-etal-2020-automatic
%X We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references. The proposed method evaluates a translation hypothesis in a regression model. The model takes the paired source, reference, and hypothesis sentence all together as an input. A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors. Our experiments show that our proposed method using Cross-lingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences. Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.
%R 10.18653/v1/2020.acl-main.327
%U https://aclanthology.org/2020.acl-main.327/
%U https://doi.org/10.18653/v1/2020.acl-main.327
%P 3553-3558
Markdown (Informal)
[Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model](https://aclanthology.org/2020.acl-main.327/) (Takahashi et al., ACL 2020)
ACL