Spoken language interaction with robots: Recommendations for future research

Marge, Matthew; Espy‐Wilson, Carol Y.; Ward, Nigel; Alwan, Abeer; Artzi, Yoav; Bansal, Mohit; Blankenship, G. L.; Chai, Joyce; Daumé, Hal; Dey, Debadeepta; Harper, Mary P.; Howard, Thomas M.; Kennington, Casey; Kruijff-Korbayová, Ivana; Manocha, Dinesh; Matuszek, Cynthia; Mead, Ross; Mooney, Raymond J.; Moore, Roger K.; Ostendorf, Mari; Pon-Barry, Heather; Rudnicky, Alexander I.; Scheutz, Matthias; Amant, Robert St.; Sun, Tanfeng; Tellex, Stefanie; Traum, David; Zhou, Yu

doi:10.1016/j.csl.2021.101255

Cited by 71 publications

(41 citation statements)

References 29 publications

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“…For speech recognition, these efforts encompass the improvement in acoustic modelling [116], [117], language modelling [118], and the whole system pipeline [119], [120] to enhance recognition accuracy. For speech synthesis, recent advancements have been made in investigating and making synthesized speech natural [121], [122], intelligible [123]- [127] and expressive [128]- [130], which will help stimulate the enthusiasm of human-machine interaction [131].…”

Section: Nlp Approachmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

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Section: Nlp Approachmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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“…The integration of social rules is a field in its own right (Charalampous et al, 2017;Marge et al, 2020;Reddy et al, 2018), where navigating and interacting with people crucial to successful task execution. It is possible to decouple these problems, but some contextual information could actually be helpful to the underlying task the agent must accomplish in the stated EVLP settings.…”

Section: Social Interactionmentioning

confidence: 99%

Core Challenges in Embodied Vision-Language Planning

Francis¹,

Kitamura²,

Labelle³

et al. 2021

Preprint

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Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

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“…The participants of the workshop identified broad areas and themes for future directions (taking note of common pitfalls (Balentine, 2007)) including applications, infrastructure, dynamic views of useragent interaction, and made several recommendations in building low-cost dialogue systems, multimodal, grounded, and situated interaction, robust and flexible dialogue management, and intelligent agents as good actors. The second workshop Spoken Language Interaction with Robots (Marge et al, 2020(Marge et al, , 2022 focused on speech and the complexities thereof when robots are involved. The participants identified recommendations relating to eight different themes:…”

Section: Recent Effortsmentioning

confidence: 99%

The State of SLIVAR: What's next for robots, human-robot interaction, and (spoken) dialogue systems?

Kennington

2021

Preprint

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We synthesize the reported results and recommendations of recent workshops and seminars that convened to discuss open questions within the important intersection of robotics, human-robot interaction, and spoken dialogue systems research. The goal of this growing area of research interest is to enable people to more effectively and naturally communicate with robots. To carry forward opportunities networking and discussion towards concrete, potentially fundable projects, we encourage interested parties to consider participating in future virtual and in-person discussions and workshops.

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Spoken language interaction with robots: Recommendations for future research

Cited by 71 publications

References 29 publications

Natural Language Processing for Smart Healthcare

Natural Language Processing for Smart Healthcare

Core Challenges in Embodied Vision-Language Planning

The State of SLIVAR: What's next for robots, human-robot interaction, and (spoken) dialogue systems?

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