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Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
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In: Conference papers (2020)
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Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ...
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What is not where: the challenge of integrating spatial representations into deep learning architectures ...
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Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
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In: Conference papers (2018)
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Abstract:
The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems.
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Keyword:
Artificial Intelligence and Robotics; Computational Linguistics; Computer Sciences; Linguistics
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URL: https://arrow.tudublin.ie/airccon/10 https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1011&context=airccon
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Back to the Future: Logic and Machine Learning
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In: Conference papers (2017)
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Towards a Computational Model of Frame of Reference Alignment in Swedish Dialogue
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In: Conference papers (2016)
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A Model for Attention-Driven Judgements in Type Theory with Records
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In: Conference papers (2016)
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