Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics
  for Machine Translation   The vast majority of evaluation metrics for machine translation are
supervised, i.e., (i) assume the existence of reference translations, (ii) are
trained on human scores, or (iii) leverage parallel data. This hinders their
applicability to cases where such supervision signals are not available. In
this work, we develop fully unsupervised evaluation metrics. To do so, we
leverage similarities and synergies between evaluation metric induction,
parallel corpus mining, and MT systems. In particular, we use an unsupervised
evaluation metric to mine pseudo-parallel data, which we use to remap deficient
underlying vector spaces (in an iterative manner) and to induce an unsupervised
MT system, which then provides pseudo-references as an additional component in
the metric. Finally, we also induce unsupervised multilingual sentence
embeddings from pseudo-parallel data. We show that our fully unsupervised
metrics are effective, i.e., they beat supervised competitors on 4 out of our 5
evaluation datasets.

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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an