43,916 research outputs found
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Do (and say) as I say: Linguistic adaptation in human-computer dialogs
© Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence structure, and acoustic features in dialog, yet little is known about how the alignment mechanism operates in the interaction between users and computer systems let alone how it may be exploited to improve the efficiency of the interaction. This article provides an account of lexical alignment in human–computer dialogs, based on empirical data collected in a simulated human–computer interaction scenario. The results indicate that alignment is present, resulting in the gradual reduction and stabilization of the vocabulary-in-use, and that it is also reciprocal. Further, the results suggest that when system and user errors occur, the development of alignment is temporarily disrupted and users tend to introduce novel words to the dialog. The results also indicate that alignment in human–computer interaction may have a strong strategic component and is used as a resource to compensate for less optimal (visually impoverished) interaction conditions. Moreover, lower alignment is associated with less successful interaction, as measured by user perceptions. The article distills the results of the study into design recommendations for human–computer dialog systems and uses them to outline a model of dialog management that supports and exploits alignment through mechanisms for in-use adaptation of the system’s grammar and lexicon
GuessWhat?! Visual object discovery through multi-modal dialogue
We introduce GuessWhat?!, a two-player guessing game as a testbed for
research on the interplay of computer vision and dialogue systems. The goal of
the game is to locate an unknown object in a rich image scene by asking a
sequence of questions. Higher-level image understanding, like spatial reasoning
and language grounding, is required to solve the proposed task. Our key
contribution is the collection of a large-scale dataset consisting of 150K
human-played games with a total of 800K visual question-answer pairs on 66K
images. We explain our design decisions in collecting the dataset and introduce
the oracle and questioner tasks that are associated with the two players of the
game. We prototyped deep learning models to establish initial baselines of the
introduced tasks.Comment: 23 pages; CVPR 2017 submission; see https://guesswhat.a
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings
in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Enactivism and Robotic Language Acquisition: A Report from the Frontier
In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio
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