Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Turn and Dialogue level User Satisfaction Estimation on
  Multi-Domain Conversations   Dialogue level quality estimation is vital for optimizing data driven
dialogue management. Current automated methods to estimate turn and dialogue
level user satisfaction employ hand-crafted features and rely on complex
annotation schemes, which reduce the generalizability of the trained models. We
propose a novel user satisfaction estimation approach which minimizes an
adaptive multi-task loss function in order to jointly predict turn-level
Response Quality labels provided by experts and explicit dialogue-level ratings
provided by end users. The proposed BiLSTM based deep neural net model
automatically weighs each turn's contribution towards the estimated
dialogue-level rating, implicitly encodes temporal dependencies, and removes
the need to hand-craft features.
  On dialogues sampled from 28 Alexa domains, two dialogue systems and three
user groups, the joint dialogue-level satisfaction estimation model achieved up
to an absolute 27% (0.43->0.70) and 7% (0.63->0.70) improvement in linear
correlation performance over baseline deep neural net and benchmark Gradient
boosting regression models, respectively.

31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio