Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
The lack of time-efficient and reliable evaluation methods hamper the development of conversational dialogue systems (chatbots). Evaluations requiring humans to converse with chatbots are time and cost-intensive, put high cognitive demands on the human judges, and yield low-quality results. In this...
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Main Authors | , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
05.10.2020
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Subjects | |
Online Access | Get full text |
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Summary: | The lack of time-efficient and reliable evaluation methods hamper the
development of conversational dialogue systems (chatbots). Evaluations
requiring humans to converse with chatbots are time and cost-intensive, put
high cognitive demands on the human judges, and yield low-quality results. In
this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust
evaluation framework that replaces human-bot conversations with conversations
between bots. Human judges then only annotate for each entity in a conversation
whether they think it is human or not (assuming there are humans participants
in these conversations). These annotations then allow us to rank chatbots
regarding their ability to mimic the conversational behavior of humans. Since
we expect that all bots are eventually recognized as such, we incorporate a
metric that measures which chatbot can uphold human-like behavior the longest,
i.e., \emph{Survival Analysis}. This metric has the ability to correlate a
bot's performance to certain of its characteristics (e.g., \ fluency or
sensibleness), yielding interpretable results. The comparably low cost of our
framework allows for frequent evaluations of chatbots during their evaluation
cycle. We empirically validate our claims by applying \emph{Spot The Bot} to
three domains, evaluating several state-of-the-art chatbots, and drawing
comparisons to related work. The framework is released as a ready-to-use tool. |
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DOI: | 10.48550/arxiv.2010.02140 |