Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Off-topic Responses to Visual Prompts   Automated methods for essay scoring have made great progress in recent years,
achieving accuracies very close to human annotators. However, a known weakness
of such automated scorers is not taking into account the semantic relevance of
the submitted text. While there is existing work on detecting answer relevance
given a textual prompt, very little previous research has been done to
incorporate visual writing prompts. We propose a neural architecture and
several extensions for detecting off-topic responses to visual prompts and
evaluate it on a dataset of texts written by language learners.

25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph