Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data Bootstrapping Recipe for Low Resource Multilingual Relation
  Classification   Relation classification (sometimes called 'extraction') requires trustworthy
datasets for fine-tuning large language models, as well as for evaluation. Data
collection is challenging for Indian languages, because they are syntactically
and morphologically diverse, as well as different from resource-rich languages
like English. Despite recent interest in deep generative models for Indian
languages, relation classification is still not well served by public data
sets. In response, we present IndoRE, a dataset with 21K entity and relation
tagged gold sentences in three Indian languages, plus English. We start with a
multilingual BERT (mBERT) based system that captures entity span positions and
type information and provides competitive monolingual relation classification.
Using this system, we explore and compare transfer mechanisms between
languages. In particular, we study the accuracy efficiency tradeoff between
expensive gold instances vs. translated and aligned 'silver' instances. We
release the dataset for future research.

25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con