Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Discourse Rewards for Document-Level Neural Machine
  Translation   Document-level machine translation focuses on the translation of entire
documents from a source to a target language. It is widely regarded as a
challenging task since the translation of the individual sentences in the
document needs to retain aspects of the discourse at document level. However,
document-level translation models are usually not trained to explicitly ensure
discourse quality. Therefore, in this paper we propose a training approach that
explicitly optimizes two established discourse metrics, lexical cohesion (LC)
and coherence (COH), by using a reinforcement learning objective. Experiments
over four different language pairs and three translation domains have shown
that our training approach has been able to achieve more cohesive and coherent
document translations than other competitive approaches, yet without
compromising the faithfulness to the reference translation. In the case of the
Zh-En language pair, our method has achieved an improvement of 2.46 percentage
points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time
improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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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: chao-dyn