Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unbabel's Participation in the WMT19 Translation Quality Estimation
  Shared Task   We present the contribution of the Unbabel team to the WMT 2019 Shared Task
on Quality Estimation. We participated on the word, sentence, and
document-level tracks, encompassing 3 language pairs: English-German,
English-Russian, and English-French. Our submissions build upon the recent
OpenKiwi framework: we combine linear, neural, and predictor-estimator systems
with new transfer learning approaches using BERT and XLM pre-trained models. We
compare systems individually and propose new ensemble techniques for word and
sentence-level predictions. We also propose a simple technique for converting
word labels into document-level predictions. Overall, our submitted systems
achieve the best results on all tracks and language pairs by a considerable
margin.

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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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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: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin