@inproceedings{bicici-2018-rtm,
title = "{RTM} results for Predicting Translation Performance",
author = "Bi{\c{c}}ici, Ergun",
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/W18-6458",
doi = "10.18653/v1/W18-6458",
pages = "765--769",
abstract = "With improved prediction combination using weights based on their training performance and stacking and multilayer perceptrons to build deeper prediction models, RTMs become the 3rd system in general at the sentence-level prediction of translation scores and achieve the lowest RMSE in English to German NMT QET results. For the document-level task, we compare document-level RTM models with sentence-level RTM models obtained with the concatenation of document sentences and obtain similar results.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bicici-2018-rtm">
<titleInfo>
<title>RTM results for Predicting Translation Performance</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ergun</namePart>
<namePart type="family">Biçici</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>With improved prediction combination using weights based on their training performance and stacking and multilayer perceptrons to build deeper prediction models, RTMs become the 3rd system in general at the sentence-level prediction of translation scores and achieve the lowest RMSE in English to German NMT QET results. For the document-level task, we compare document-level RTM models with sentence-level RTM models obtained with the concatenation of document sentences and obtain similar results.</abstract>
<identifier type="citekey">bicici-2018-rtm</identifier>
<identifier type="doi">10.18653/v1/W18-6458</identifier>
<location>
<url>https://aclanthology.org/W18-6458</url>
</location>
<part>
<date>2018-oct</date>
<extent unit="page">
<start>765</start>
<end>769</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RTM results for Predicting Translation Performance
%A Biçici, Ergun
%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 bicici-2018-rtm
%X With improved prediction combination using weights based on their training performance and stacking and multilayer perceptrons to build deeper prediction models, RTMs become the 3rd system in general at the sentence-level prediction of translation scores and achieve the lowest RMSE in English to German NMT QET results. For the document-level task, we compare document-level RTM models with sentence-level RTM models obtained with the concatenation of document sentences and obtain similar results.
%R 10.18653/v1/W18-6458
%U https://aclanthology.org/W18-6458
%U https://doi.org/10.18653/v1/W18-6458
%P 765-769
Markdown (Informal)
[RTM results for Predicting Translation Performance](https://aclanthology.org/W18-6458) (Biçici, 2018)
ACL