Active learning for example-based dialog systems

T Hiraoka, G Neubig, K Yoshino, T Toda… - Dialogues with Social …, 2017 - Springer
Dialogues with Social Robots: Enablements, Analyses, and Evaluation, 2017Springer
While example-based dialog is a popular option for the construction of dialog systems,
creating example bases for a specific task or domain requires significant human effort. To
reduce this human effort, in this paper, we propose an active learning framework to construct
example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling
strategies for selecting inputs to present to human annotators who create system responses
for the selected inputs. We compare performance of these proposed strategies with a …
Abstract
While example-based dialog is a popular option for the construction of dialog systems, creating example bases for a specific task or domain requires significant human effort. To reduce this human effort, in this paper, we propose an active learning framework to construct example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling strategies for selecting inputs to present to human annotators who create system responses for the selected inputs. We compare performance of these proposed strategies with a random selection strategy in simulation-based evaluation on 6 different domains. Evaluation results show that the proposed strategies are good alternatives to random selection in domains where the complexity of system utterances is low.
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