AICLOct 5, 2020

Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems

arXiv:2010.02140v11005 citations
Originality Incremental advance
AI Analysis

This addresses the time and cost inefficiencies in chatbot evaluation for developers and researchers, though it is an incremental improvement over existing evaluation methods.

The paper tackles the problem of evaluating conversational dialogue systems by introducing Spot The Bot, a cost-efficient framework that replaces human-bot conversations with bot-bot interactions and uses human annotations to rank chatbots based on their ability to mimic human behavior, validated across three domains with state-of-the-art chatbots.

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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