Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do Transformer Attention Heads Provide Transparency in Abstractive
  Summarization?   Learning algorithms become more powerful, often at the cost of increased
complexity. In response, the demand for algorithms to be transparent is
growing. In NLP tasks, attention distributions learned by attention-based deep
learning models are used to gain insights in the models' behavior. To which
extent is this perspective valid for all NLP tasks? We investigate whether
distributions calculated by different attention heads in a transformer
architecture can be used to improve transparency in the task of abstractive
summarization. To this end, we present both a qualitative and quantitative
analysis to investigate the behavior of the attention heads. We show that some
attention heads indeed specialize towards syntactically and semantically
distinct input. We propose an approach to evaluate to which extent the
Transformer model relies on specifically learned attention distributions. We
also discuss what this implies for using attention distributions as a means of
transparency.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
19
The categories of this paper are: eess