Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Zero-Shot Multi-Lingual Entity Linking   Entity linking -- the task of identifying references in free text to relevant
knowledge base representations -- often focuses on single languages. We
consider multilingual entity linking, where a single model is trained to link
references to same-language knowledge bases in several languages. We propose a
neural ranker architecture, which leverages multilingual transformer
representations of text to be easily applied to a multilingual setting. We then
explore how a neural ranker trained in one language (e.g. English) transfers to
an unseen language (e.g. Chinese), and find that while there is a consistent
but not large drop in performance. How can this drop in performance be
alleviated? We explore adding an adversarial objective to force our model to
learn language-invariant representations. We find that using this approach
improves recall in several datasets, often matching the in-language
performance, thus alleviating some of the performance loss occurring from
zero-shot transfer.

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The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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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: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc