@inproceedings{shimanaka-etal-2018-ruse,
title = "{RUSE}: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation",
author = "Shimanaka, Hiroki and
Kajiwara, Tomoyuki and
Komachi, Mamoru",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6456",
doi = "10.18653/v1/W18-6456",
pages = "751--758",
abstract = "We introduce the RUSE metric for the WMT18 metrics shared task. Sentence embeddings can capture global information that cannot be captured by local features based on character or word N-grams. Although training sentence embeddings using small-scale translation datasets with manual evaluation is difficult, sentence embeddings trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. We use a multi-layer perceptron regressor based on three types of sentence embeddings. The experimental results of the WMT16 and WMT17 datasets show that the RUSE metric achieves a state-of-the-art performance in both segment- and system-level metrics tasks with embedding features only.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shimanaka-etal-2018-ruse">
<titleInfo>
<title>RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hiroki</namePart>
<namePart type="family">Shimanaka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomoyuki</namePart>
<namePart type="family">Kajiwara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-oct</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Conference on Machine Translation: Shared Task Papers</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Belgium, Brussels</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We introduce the RUSE metric for the WMT18 metrics shared task. Sentence embeddings can capture global information that cannot be captured by local features based on character or word N-grams. Although training sentence embeddings using small-scale translation datasets with manual evaluation is difficult, sentence embeddings trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. We use a multi-layer perceptron regressor based on three types of sentence embeddings. The experimental results of the WMT16 and WMT17 datasets show that the RUSE metric achieves a state-of-the-art performance in both segment- and system-level metrics tasks with embedding features only.</abstract>
<identifier type="citekey">shimanaka-etal-2018-ruse</identifier>
<identifier type="doi">10.18653/v1/W18-6456</identifier>
<location>
<url>https://aclanthology.org/W18-6456</url>
</location>
<part>
<date>2018-oct</date>
<extent unit="page">
<start>751</start>
<end>758</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
%A Shimanaka, Hiroki
%A Kajiwara, Tomoyuki
%A Komachi, Mamoru
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F shimanaka-etal-2018-ruse
%X We introduce the RUSE metric for the WMT18 metrics shared task. Sentence embeddings can capture global information that cannot be captured by local features based on character or word N-grams. Although training sentence embeddings using small-scale translation datasets with manual evaluation is difficult, sentence embeddings trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. We use a multi-layer perceptron regressor based on three types of sentence embeddings. The experimental results of the WMT16 and WMT17 datasets show that the RUSE metric achieves a state-of-the-art performance in both segment- and system-level metrics tasks with embedding features only.
%R 10.18653/v1/W18-6456
%U https://aclanthology.org/W18-6456
%U https://doi.org/10.18653/v1/W18-6456
%P 751-758
Markdown (Informal)
[RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation](https://aclanthology.org/W18-6456) (Shimanaka et al., 2018)
ACL