Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Attentive Ranking Networks for Learning to Order Sentences   We present an attention-based ranking framework for learning to order
sentences given a paragraph. Our framework is built on a bidirectional sentence
encoder and a self-attention based transformer network to obtain an input order
invariant representation of paragraphs. Moreover, it allows seamless training
using a variety of ranking based loss functions, such as pointwise, pairwise,
and listwise ranking. We apply our framework on two tasks: Sentence Ordering
and Order Discrimination. Our framework outperforms various state-of-the-art
methods on these tasks on a variety of evaluation metrics. We also show that it
achieves better results when using pairwise and listwise ranking losses, rather
than the pointwise ranking loss, which suggests that incorporating relative
positions of two or more sentences in the loss function contributes to better
learning.

19
The categories of this paper are: eess
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph