Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Simultaneous Speech Translation Using Alignment-Based Chunking   In simultaneous machine translation, the objective is to determine when to
produce a partial translation given a continuous stream of source words, with a
trade-off between latency and quality. We propose a neural machine translation
(NMT) model that makes dynamic decisions when to continue feeding on input or
generate output words. The model is composed of two main components: one to
dynamically decide on ending a source chunk, and another that translates the
consumed chunk. We train the components jointly and in a manner consistent with
the inference conditions. To generate chunked training data, we propose a
method that utilizes word alignment while also preserving enough context. We
compare models with bidirectional and unidirectional encoders of different
depths, both on real speech and text input. Our results on the IWSLT 2020
English-to-German task outperform a wait-k baseline by 2.6 to 3.7% BLEU
absolute.

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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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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: physics
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The categories of this paper are: hep-ph