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The Rapidly Changing Landscape of Conversational Agents
[article]
<span title="2018-03-24">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2018-03-22">2018</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.08419v1">arXiv:1803.08419v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2h4dlfn7ffbudmbgcmjtksnz4m">fatcat:2h4dlfn7ffbudmbgcmjtksnz4m</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.08419v1">arXiv:1803.08419v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2h4dlfn7ffbudmbgcmjtksnz4m">fatcat:2h4dlfn7ffbudmbgcmjtksnz4m</a> </span>
Conversational agents have become ubiquitous, ranging from goal-oriented systems for helping with reservations to chit-chat models found in modern virtual assistants. In this survey paper, we explore this fascinating field. We look at some of the pioneering work that defined the field and gradually move to the current state-of-the-art models. We look at statistical, neural, generative adversarial network based and reinforcement learning based approaches and how they evolved. Along the way we
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... cuss various challenges that the field faces, lack of context in utterances, not having a good quantitative metric to compare models, lack of trust in agents because they do not have a consistent persona etc. We structure this paper in a way that answers these pertinent questions and discusses competing approaches to solve them.
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