9,857 research outputs found
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Learning language through pictures
We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently predict its visual representation and the next word in the
sentence. Mimicking an important aspect of human language learning, it acquires
meaning representations for individual words from descriptions of visual
scenes. Moreover, it learns to effectively use sequential structure in semantic
interpretation of multi-word phrases.Comment: To appear at ACL 201
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Directional adposition use in English, Swedish and Finnish
Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellÀ (in front of) and jÀljessÀ (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003).
When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellÀ (in front of) and jÀljessÀ (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected.
We asked native English, Swedish and Finnish speakersâ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers.
All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion.
We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion.
Levinson, S. C. (1996). Frames of reference and Molyneuxâs question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press.
Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press.
Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Building a Multimodal Lexicon: Lessons from Infants' Learning of Body Part Words
Human children outperform artificial learners because the former quickly acquire a multimodal, syntactically informed, and ever-growing lexicon with little evidence. Most of this lexicon is unlabelled and processed with unsupervised mechanisms, leading to robust and generalizable knowledge. In this paper, we summarize results related to 4-month-oldsâ learning of body part words. In addition to providing direct experimental evidence on some of the Workshopâs assumptions, we suggest several avenues of research that may be useful to those developing and testing artificial learners. A first set of studies using a controlled laboratory learning paradigm shows that human infants learn better from tactile-speech than visual-speech co-occurrences, suggesting that the signal/modality should be considered when designing and exploiting multimodal learning tasks. A series of observational studies document the ways in which parents naturally structure the multimodal information they provide for infants, which probably happens in lexically specific ways. Finally, our results suggest that 4-month-olds can pick up on co-occurrences between words and specific touch locations (a prerequisite of learning an association between a body part word and the referent on the childâs own body) after very brief exposures, which we interpret as most compatible with unsupervised predictive models of learning
Integrating Across Conceptual Spaces
It has been shown that structure is shared across multiple modalities in the real world: if we speak about two items in similar ways, then they are also likely to appear in similar visual contexts. Such similarity relationships are recapitulated across modalities for entire systems of concepts. This provides a signal that can be used to identify the correct mapping between modalities without relying on event-based learning, by a process of systems alignment. Because it depends on relationships within a modality, systems alignment can operate asynchronously, meaning that learning may not require direct labelling events (e.g., seeing a truck and hearing someone say the word âtruckâ). Instead, learning can occur based on linguistic and visual information which is received at different points in time (e.g., having overheard a conversation about trucks, and seeing one on the road the next day).
This thesis explores the value of alignment in learning to integrate between conceptual systems. It takes a joint experimental and computational approach, which simultaneously facilitates insights on alignment processes in controlled environments and at scale.
The role of alignment in learning is explored from three perspectives, yielding three distinct contributions. In Chapter 2, signatures of alignment are identified in a real-world setting: childrenâs early concept learning. Moving to a controlled experimental setting, Chapter 3 demonstrates that humans benefit from alignment signals in cross-system learning, and finds that models which attempt the asynchronous alignment of systems best capture human behaviour. Chapter 4 implements these insights in machine-learning systems, using alignment to tackle cross-modal learning problems at scale.
Alignment processes prove valuable to human learning across conceptual systems, providing a fresh perspective on learning that complements prevailing event-based accounts. This research opens doors for machine learning systems to harness alignment mechanisms for cross-modal learning, thus reducing their reliance on extensive supervision by drawing inspiration from both human learning and the structure of the environment
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