Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multilingual Neural Semantic Parsing for Low-Resourced Languages   Multilingual semantic parsing is a cost-effective method that allows a single
model to understand different languages. However, researchers face a great
imbalance of availability of training data, with English being resource rich,
and other languages having much less data. To tackle the data limitation
problem, we propose using machine translation to bootstrap multilingual
training data from the more abundant English data. To compensate for the data
quality of machine translated training data, we utilize transfer learning from
pretrained multilingual encoders to further improve the model. To evaluate our
multilingual models on human-written sentences as opposed to machine translated
ones, we introduce a new multilingual semantic parsing dataset in English,
Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset.
We show that joint multilingual training with pretrained encoders substantially
outperforms our baselines on the TOP dataset and outperforms the
state-of-the-art model on the public NLMaps dataset. We also establish a new
baseline for zero-shot learning on the TOP dataset. We find that a semantic
parser trained only on English data achieves a zero-shot performance of 44.9%
exact-match accuracy on Italian sentences.

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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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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: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org