Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Event Representation via Simultaneous Weakly Supervised
  Contrastive Learning and Clustering   Representations of events described in text are important for various tasks.
In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive
learning and Clustering framework for event representation learning. SWCC
learns event representations by making better use of co-occurrence information
of events. Specifically, we introduce a weakly supervised contrastive learning
method that allows us to consider multiple positives and multiple negatives,
and a prototype-based clustering method that avoids semantically related events
being pulled apart. For model training, SWCC learns representations by
simultaneously performing weakly supervised contrastive learning and
prototype-based clustering. Experimental results show that SWCC outperforms
other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In
addition, a thorough analysis of the prototype-based clustering method
demonstrates that the learned prototype vectors are able to implicitly capture
various relations between events.

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The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex