Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MoverScore: Text Generation Evaluating with Contextualized Embeddings
  and Earth Mover Distance   A robust evaluation metric has a profound impact on the development of text
generation systems. A desirable metric compares system output against
references based on their semantics rather than surface forms. In this paper we
investigate strategies to encode system and reference texts to devise a metric
that shows a high correlation with human judgment of text quality. We validate
our new metric, namely MoverScore, on a number of text generation tasks
including summarization, machine translation, image captioning, and
data-to-text generation, where the outputs are produced by a variety of neural
and non-neural systems. Our findings suggest that metrics combining
contextualized representations with a distance measure perform the best. Such
metrics also demonstrate strong generalization capability across tasks. For
ease-of-use we make our metrics available as web service.

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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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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: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: econ