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Abstract
Research at the Interaction Lab focuses on human-agent communication using conversational Natural Language. The ultimate goal is to create systems where humans and AI agents (including embodied robots) can spontaneously form teams and coordinate shared tasks through the use of Natural Language conversation as a universal communication interface. This paper first introduces machine learning approaches to problems in conversational AI in general, where computational agents must coordinate with humans to solve tasks using conversational Natural Language. It also covers some of the practical systems developed in the Interaction Lab, ranging from speech interfaces on smart speakers to embodied robots interacting using visually grounded language. In several cases communication between multiple agents is addressed. The paper surveys the central research problems addressed here, the approaches developed, and our main results. Some key open research questions and directions are then discussed, leading towards a future vision of conversational, collaborative multi-agent systems.
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Affiliation(s)
- Oliver Lemon
- School of Mathematical and Computer Sciences, Heriot-Watt University, United Kingdom
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Uc-Cetina V, Navarro-Guerrero N, Martin-Gonzalez A, Weber C, Wermter S. Survey on reinforcement learning for language processing. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10205-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractIn recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language processing (NLP) tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of NLP, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in NLP that might benefit from RL.
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Kumagai K, Kobayashi I, Mochihashi D, Asoh H, Nakamura T, Nagai T. Natural Language Generation Using Monte Carlo Tree Search. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2018. [DOI: 10.20965/jaciii.2018.p0777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We propose a method of simulation-based natural language generation that accounts for both building a correct syntactic structure and reflecting the given situational information as input for the generated sentence. We employ the Monte Carlo tree search for this nontrivial search problem in simulation, using context-free grammar rules as search operators. We evaluated numerous generation results from two aspects: the appropriateness of sentence contents for the given input information and the sequence of words in a generated sentence. Furthermore, in order to realize an efficient search in simulation, we introduced procedures to unfold syntactic structures from words strongly related to the given situational information, and increased the probability of selecting those related words. Through a numbers of experiments, we confirmed that our method can effectively generate a sentence with various words and phrasings.
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Maekawa T, Takano W. Spatial representation of context-dependent sentences and its application to sentence generation. Adv Robot 2017. [DOI: 10.1080/01691864.2017.1351392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Tomoyuki Maekawa
- Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan
| | - Wataru Takano
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
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Griol D, Patricio MÁ, Molina JM. CALIMACO: desarrollo de un servicio de bibliotecario virtual para la interacción multimodal con dispositivos móviles. REVISTA ESPANOLA DE DOCUMENTACION CIENTIFICA 2016. [DOI: 10.3989/redc.2016.2.1262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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Griol D, Molina JM, Callejas Z. A proposal for the development of adaptive spoken interfaces to access the Web. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.087] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Gargett A, Barnden J. Gen-Meta: Generating metaphors by combining AI and corpus-based modeling. WEB INTELLIGENCE 2015. [DOI: 10.3233/web-150313] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Andrew Gargett
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom. E-mails: ,
| | - John Barnden
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom. E-mails: ,
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Griol D, Callejas Z, López-Cózar R, Riccardi G. A domain-independent statistical methodology for dialog management in spoken dialog systems. COMPUT SPEECH LANG 2014. [DOI: 10.1016/j.csl.2013.09.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Expanding Interaction Potentials within Virtual Environments: Investigating the Usability of Speech and Manual Input Modes for Decoupled Interaction. ADVANCES IN HUMAN-COMPUTER INTERACTION 2011. [DOI: 10.1155/2011/565689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Distributed technologies and ubiquitous computing now support users who may be detached or decoupled from traditional interactions. In order to investigate the potential usability of speech and manual input devices, an evaluation of speech input across different user groups and a usability assessment of independent-user and collaborative-user interactions was conducted. Whilst the primary focus was on a formative usability evaluation, the user group evaluation provided a formal basis to underpin the academic rigor of the exercise. The results illustrate that using a speech interface is important in understanding user acceptance of such technologies. From the usability assessment it was possible to translate interactions and make them compatible with innovative input devices. This approach to interaction is still at an early stage of development, and the potential or validity of this interfacing concept is still under evaluation; however, as a concept demonstrator, the results of these initial evaluations demonstrate the potential usability issues of both input devices as well as highlighting their suitability for advanced virtual applications.
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