DeepAI AI Chat
Log In Sign Up

Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates

by   Joo-Kyung Kim, et al.

In domain classification for spoken dialog systems, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems. Previous work usually utilizes OOD detectors that are trained separately from in-domain (IND) classifiers, and confidence thresholding for OOD detection given target evaluation scores. In this paper, we introduce a neural joint learning model for domain classification and OOD detection, where dynamic class weighting is used during the model training to satisfice a given OOD false acceptance rate (FAR) while maximizing the domain classification accuracy. Evaluating on two domain classification tasks for the utterances from a large spoken dialogue system, we show that our approach significantly improves the domain classification performance with satisficing given target FARs.


page 1

page 2

page 3

page 4


Supervised Domain Enablement Attention for Personalized Domain Classification

In large-scale domain classification for natural language understanding,...

Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia

Users of spoken dialogue systems (SDS) expect high quality interactions ...

Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification

In large-scale domain classification, an utterance can be handled by mul...

Ship Detection: Parameter Server Variant

Deep learning ship detection in satellite optical imagery suffers from f...

Unsupervised Spoken Utterance Classification

An intelligent virtual assistant (IVA) enables effortless conversations ...

VFNet: A Convolutional Architecture for Accent Classification

Understanding accent is an issue which can derail any human-machine inte...