Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: COMET: A Neural Framework for MT Evaluation   We present COMET, a neural framework for training multilingual machine
translation evaluation models which obtains new state-of-the-art levels of
correlation with human judgements. Our framework leverages recent breakthroughs
in cross-lingual pretrained language modeling resulting in highly multilingual
and adaptable MT evaluation models that exploit information from both the
source input and a target-language reference translation in order to more
accurately predict MT quality. To showcase our framework, we train three models
with different types of human judgements: Direct Assessments, Human-mediated
Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve
new state-of-the-art performance on the WMT 2019 Metrics shared task and
demonstrate robustness to high-performing systems.

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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th