Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fusing Context Into Knowledge Graph for Commonsense Question Answering   Commonsense question answering (QA) requires a model to grasp commonsense and
factual knowledge to answer questions about world events. Many prior methods
couple language modeling with knowledge graphs (KG). However, although a KG
contains rich structural information, it lacks the context to provide a more
precise understanding of the concepts. This creates a gap when fusing knowledge
graphs into language modeling, especially when there is insufficient labeled
data. Thus, we propose to employ external entity descriptions to provide
contextual information for knowledge understanding. We retrieve descriptions of
related concepts from Wiktionary and feed them as additional input to
pre-trained language models. The resulting model achieves state-of-the-art
result in the CommonsenseQA dataset and the best result among non-generative
models in OpenBookQA.

12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph