Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context-Aware Neural Machine Translation Learns Anaphora Resolution   Standard machine translation systems process sentences in isolation and hence
ignore extra-sentential information, even though extended context can both
prevent mistakes in ambiguous cases and improve translation coherence. We
introduce a context-aware neural machine translation model designed in such way
that the flow of information from the extended context to the translation model
can be controlled and analyzed. We experiment with an English-Russian subtitles
dataset, and observe that much of what is captured by our model deals with
improving pronoun translation. We measure correspondences between induced
attention distributions and coreference relations and observe that the model
implicitly captures anaphora. It is consistent with gains for sentences where
pronouns need to be gendered in translation. Beside improvements in anaphoric
cases, the model also improves in overall BLEU, both over its context-agnostic
version (+0.7) and over simple concatenation of the context and source
sentences (+0.6).

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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph