Conversational In-Vehicle Dialog Systems: The past, present, and future

Weng, Fuliang; Angkititrakul, Pongtep; Shriberg, Elizabeth; Heck, Larry; Peters, Stanley; Hansen, John H. L.

doi:10.1109/msp.2016.2599201

Cited by 40 publications

(13 citation statements)

References 19 publications

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“…Such conversational interfaces already enable people to interact with wearables, robots, and other smart devices in a natural way, as if they were talking with another person [2]. When successfully applied in autonomous vehicles, those systems can assist the driver in a variety of tasks [3]. While the technology necessary for implementing such conversational interfaces is under development [2,4], the discussion on how knowledge about social interactions between humans and other technologies can be used in this domain has recently begun [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Ruijten

Terken

Chandramouli

2018

MTI

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Autonomous vehicles use sensors and artificial intelligence to drive themselves. Surveys indicate that people are fascinated by the idea of autonomous driving, but are hesitant to relinquish control of the vehicle. Lack of trust seems to be the core reason for these concerns. In order to address this, an intelligent agent approach was implemented, as it has been argued that human traits increase trust in interfaces. Where other approaches mainly use anthropomorphism to shape appearances, the current approach uses anthropomorphism to shape the interaction, applying Gricean maxims (i.e., guidelines for effective conversation). The contribution of this approach was tested in a simulator that employed both a graphical and a conversational user interface, which were rated on likability, perceived intelligence, trust, and anthropomorphism. Results show that the conversational interface was trusted, liked, and anthropomorphized more, and was perceived as more intelligent, than the graphical user interface. Additionally, an interface that was portrayed as more confident in making decisions scored higher on all four constructs than one that was portrayed as having low confidence. These results together indicate that equipping autonomous vehicles with interfaces that mimic human behavior may help increasing people’s trust in, and, consequently, their acceptance of them.

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Section: Introductionmentioning

confidence: 99%

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Ruijten

Terken

Chandramouli

2018

MTI

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“…In-vehicle dialog systems are a perfect case study for this. In addition to the challenges faced by general-purpose dialog systems, in-vehicle dialog systems face additional concerns regarding the added variables of the driver and the environment (Weng et al 2016). As the focus and safety of the driver is paramount, dialog systems must be cognizant of what in-formation it surfaces; it should not inform a driver to take a "sharp left" while moving at a certain speed, or play loud sounds that could startle a driver.…”

Section: Safetymentioning

confidence: 99%

Ethical Challenges in Data-Driven Dialogue Systems

Henderson

Sinha

Angelard-Gontier

et al. 2018

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

121

101

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The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.

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“…• Last but not least, the proposed approach can be applied to different applications, beyond chitchat dialogue. Example applications in no particular order are as follows: combining task-oriented dialogue with open-ended dialogue [50], strategic dialogue [51], spatially-aware dialogue [52], automatic (medical) diagnosis [53], in-car infotainment systems [54], and conversational robots [55], among others.…”

Section: Human Evaluationmentioning

confidence: 99%

Ensemble-based deep reinforcement learning for chatbots

et al. 2019

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Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this challenge, but its successful application remains an open question. This article describes a novel ensemble-based approach applied to value-based DRL chatbots, which use finite action sets as a form of meaning representation. In our approach, while dialogue actions are derived from sentence clustering, the training datasets in our ensemble are derived from dialogue clustering. The latter aim to induce specialised agents that learn to interact in a particular style. In order to facilitate neural chatbot training using our proposed approach, we assume dialogue data in raw text onlywithout any manually-labelled data. Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent. In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency -which revealed that our proposed dialogue rewards strongly correlate with human judgements. 1 1 Work carried out while the first author was visiting Samsung Research. 1

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Conversational In-Vehicle Dialog Systems: The past, present, and future

Cited by 40 publications

References 19 publications

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Ethical Challenges in Data-Driven Dialogue Systems

Ensemble-based deep reinforcement learning for chatbots

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