942 research outputs found
Predicting Causes of Reformulation in Intelligent Assistants
Intelligent assistants (IAs) such as Siri and Cortana conversationally
interact with users and execute a wide range of actions (e.g., searching the
Web, setting alarms, and chatting). IAs can support these actions through the
combination of various components such as automatic speech recognition, natural
language understanding, and language generation. However, the complexity of
these components hinders developers from determining which component causes an
error. To remove this hindrance, we focus on reformulation, which is a useful
signal of user dissatisfaction, and propose a method to predict the
reformulation causes. We evaluate the method using the user logs of a
commercial IA. The experimental results have demonstrated that features
designed to detect the error of a specific component improve the performance of
reformulation cause detection.Comment: 11 pages, 2 figures, accepted as a long paper for SIGDIAL 201
Report drawn up on behalf of the Committee on Energy and Research on the proposal from the Commission of the European Comnunities to the Council (Doc. 1-433/81) for a decision adopting a research and training programne (1982-1986) in the field of controlled thermonuclear fusion. EP Working Documents, document 1-1080/81, 8 March 1982.
Report drawn up on behalf of the Committee on Energy and Research on the proposal from the Commission of the European Communities to the Council (Doc. 1-627/79) for a Regulation amending Regulation No 726/79 as regards the granting of financial support for projects to exploit alternative energy sources. EP Working Documents 1980-1981, Document 1-214/80, 9 June 1980
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