Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lexical Knowledge Internalization for Neural Dialog Generation   We propose knowledge internalization (KI), which aims to complement the
lexical knowledge into neural dialog models. Instead of further conditioning
the knowledge-grounded dialog (KGD) models on externally retrieved knowledge,
we seek to integrate knowledge about each input token internally into the
model's parameters. To tackle the challenge due to the large scale of lexical
knowledge, we adopt the contrastive learning approach and create an effective
token-level lexical knowledge retriever that requires only weak supervision
mined from Wikipedia. We demonstrate the effectiveness and general
applicability of our approach on various datasets and diversified model
structures.

28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph