Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RoBLEURT Submission for the WMT2021 Metrics Task   In this paper, we present our submission to Shared Metrics Task: RoBLEURT
(Robustly Optimizing the training of BLEURT). After investigating the recent
advances of trainable metrics, we conclude several aspects of vital importance
to obtain a well-performed metric model by: 1) jointly leveraging the
advantages of source-included model and reference-only model, 2) continuously
pre-training the model with massive synthetic data pairs, and 3) fine-tuning
the model with data denoising strategy. Experimental results show that our
model reaching state-of-the-art correlations with the WMT2020 human annotations
upon 8 out of 10 to-English language pairs.

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The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an