The Rapidly Changing Landscape of Conversational Agents

By Vinayak Mathur, Arpit Singh. 2018

10 citations recorded by the source corpus.

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 discuss 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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. João Sedoc
2. Lyle Ungar
3. Tianlin Liu
4. Peter Christen

Citations in the corpus

• Continual Learning for Sentence Representations Using Conceptors (2019, background): “However, in open-domain NLP systems such as conversational agents, oftentimes we are facing a dynamic environment, where training data are accumulated sequentially over time and the distributions of training data vary with respect to external input (Lee, 2017; Mathur and Singh, 2018).”