Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Compare Aggregate Transformer for Understanding Document-grounded
  Dialogue   Unstructured documents serving as external knowledge of the dialogues help to
generate more informative responses. Previous research focused on knowledge
selection (KS) in the document with dialogue. However, dialogue history that is
not related to the current dialogue may introduce noise in the KS processing.
In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly
denoise the dialogue context and aggregate the document information for
response generation. We designed two different comparison mechanisms to reduce
noise (before and during decoding). In addition, we propose two metrics for
evaluating document utilization efficiency based on word overlap. Experimental
results on the CMUDoG dataset show that the proposed CAT model outperforms the
state-of-the-art approach and strong baselines.

15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math