Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparison and Combination of Sentence Embeddings Derived from Different
  Supervision Signals   We have recently seen many successful applications of sentence embedding
methods. It has not been well understood, however, what kind of properties are
captured in the resulting sentence embeddings, depending on the supervision
signals. In this paper, we focus on two types of sentence embeddings obtained
by using natural language inference (NLI) datasets and definition sentences
from a word dictionary and investigate their properties by comparing their
performance with the semantic textual similarity (STS) task using the STS data
partitioned by two perspectives: 1) the sources of sentences, and 2) the
superficial similarity of the sentence pairs, and their performance on the
downstream and probing tasks. We also demonstrate that combining the two types
of embeddings yields substantially better performances than respective models
on unsupervised STS tasks and downstream tasks.

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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an