Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-Resource Corpus Filtering using Multilingual Sentence Embeddings   In this paper, we describe our submission to the WMT19 low-resource parallel
corpus filtering shared task. Our main approach is based on the LASER toolkit
(Language-Agnostic SEntence Representations), which uses an encoder-decoder
architecture trained on a parallel corpus to obtain multilingual sentence
representations. We then use the representations directly to score and filter
the noisy parallel sentences without additionally training a scoring function.
We contrast our approach to other promising methods and show that LASER yields
strong results. Finally, we produce an ensemble of different scoring methods
and obtain additional gains. Our submission achieved the best overall
performance for both the Nepali-English and Sinhala-English 1M tasks by a
margin of 1.3 and 1.4 BLEU respectively, as compared to the second best
systems. Moreover, our experiments show that this technique is promising for
low and even no-resource scenarios.

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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: math