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AI Magazine
Article . 2022 . Peer-reviewed
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AI Magazine
Article . 2022
License: CC BY
Data sources: AI Magazine
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Article . 2022
Data sources: DBLP
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The success of Conversational AI and the AI evaluation challenge it reveals

Authors: Ian Beaver;

The success of Conversational AI and the AI evaluation challenge it reveals

Abstract

Abstract Research interest in Conversational artificial intelligence (ConvAI) has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more “human” tasks that involve creativity and variation, evaluation strategies need to mature with them.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
hybrid