Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting Transformers with KNN-Based Composite Memory for Dialogue   Various machine learning tasks can benefit from access to external
information of different modalities, such as text and images. Recent work has
focused on learning architectures with large memories capable of storing this
knowledge. We propose augmenting generative Transformer neural networks with
KNN-based Information Fetching (KIF) modules. Each KIF module learns a read
operation to access fixed external knowledge. We apply these modules to
generative dialog modeling, a challenging task where information must be
flexibly retrieved and incorporated to maintain the topic and flow of
conversation. We demonstrate the effectiveness of our approach by identifying
relevant knowledge required for knowledgeable but engaging dialog from
Wikipedia, images, and human-written dialog utterances, and show that
leveraging this retrieved information improves model performance, measured by
automatic and human evaluation.

11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph