Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wasserstein distances for evaluating cross-lingual embeddings   Word embeddings are high dimensional vector representations of words that
capture their semantic similarity in the vector space. There exist several
algorithms for learning such embeddings both for a single language as well as
for several languages jointly. In this work we propose to evaluate collections
of embeddings by adapting downstream natural language tasks to the optimal
transport framework. We show how the family of Wasserstein distances can be
used to solve cross-lingual document retrieval and the cross-lingual document
classification problems. We argue on the advantages of this approach compared
to more traditional evaluation methods of embeddings like bilingual lexical
induction. Our experimental results suggest that using Wasserstein distances on
these problems out-performs several strong baselines and performs on par with
state-of-the-art models.

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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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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: physics