Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Transferability of Neural Models of Morphological Analogies   Analogical proportions are statements expressed in the form "A is to B as C
is to D" and are used for several reasoning and classification tasks in
artificial intelligence and natural language processing (NLP). In this paper,
we focus on morphological tasks and we propose a deep learning approach to
detect morphological analogies. We present an empirical study to see how our
framework transfers across languages, and that highlights interesting
similarities and differences between these languages. In view of these results,
we also discuss the possibility of building a multilingual morphological model.

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The categories of this paper are: cond-mat
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol