Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metric Learning for Dynamic Text Classification   Traditional text classifiers are limited to predicting over a fixed set of
labels. However, in many real-world applications the label set is frequently
changing. For example, in intent classification, new intents may be added over
time while others are removed. We propose to address the problem of dynamic
text classification by replacing the traditional, fixed-size output layer with
a learned, semantically meaningful metric space. Here the distances between
textual inputs are optimized to perform nearest-neighbor classification across
overlapping label sets. Changing the label set does not involve removing
parameters, but rather simply adding or removing support points in the metric
space. Then the learned metric can be fine-tuned with only a few additional
training examples. We demonstrate that this simple strategy is robust to
changes in the label space. Furthermore, our results show that learning a
non-Euclidean metric can improve performance in the low data regime, suggesting
that further work on metric spaces may benefit low-resource research.

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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
3
The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th