Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparing in context: Improving cosine similarity measures with a metric
  tensor   Cosine similarity is a widely used measure of the relatedness of pre-trained
word embeddings, trained on a language modeling goal. Datasets such as
WordSim-353 and SimLex-999 rate how similar words are according to human
annotators, and as such are often used to evaluate the performance of language
models. Thus, any improvement on the word similarity task requires an improved
word representation. In this paper, we propose instead the use of an extended
cosine similarity measure to improve performance on that task, with gains in
interpretability. We explore the hypothesis that this approach is particularly
useful if the word-similarity pairs share the same context, for which distinct
contextualized similarity measures can be learned. We first use the dataset of
Richie et al. (2020) to learn contextualized metrics and compare the results
with the baseline values obtained using the standard cosine similarity measure,
which consistently shows improvement. We also train a contextualized similarity
measure for both SimLex-999 and WordSim-353, comparing the results with the
corresponding baselines, and using these datasets as independent test sets for
the all-context similarity measure learned on the contextualized dataset,
obtaining positive results for a number of tests.

27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph