Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imposing Relation Structure in Language-Model Embeddings Using
  Contrastive Learning   Though language model text embeddings have revolutionized NLP research, their
ability to capture high-level semantic information, such as relations between
entities in text, is limited. In this paper, we propose a novel contrastive
learning framework that trains sentence embeddings to encode the relations in a
graph structure. Given a sentence (unstructured text) and its graph, we use
contrastive learning to impose relation-related structure on the token-level
representations of the sentence obtained with a CharacterBERT (El Boukkouri et
al.,2020) model. The resulting relation-aware sentence embeddings achieve
state-of-the-art results on the relation extraction task using only a simple
KNN classifier, thereby demonstrating the success of the proposed method.
Additional visualization by a tSNE analysis shows the effectiveness of the
learned representation space compared to baselines. Furthermore, we show that
we can learn a different space for named entity recognition, again using a
contrastive learning objective, and demonstrate how to successfully combine
both representation spaces in an entity-relation task.

16
The categories of this paper are: stat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph