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- W4297790983 abstract "Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the data used for learning consists almost entirely of textual descriptions, which tend to be cleaner, clearer, and more grammatical than actual human interactions. In this work, we present the Grounded Language Dataset (GoLD), a multimodal dataset of common household objects described by people using either spoken or written language. We analyze the differences and present an experiment showing how the different modalities affect language learning from human in-put. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, text, and speech interact, as well as show differences in the vernacular of these modalities impact results." @default.
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- W4297790983 date "2020-07-29" @default.
- W4297790983 modified "2023-10-18" @default.
- W4297790983 title "Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning" @default.
- W4297790983 doi "https://doi.org/10.48550/arxiv.2007.14987" @default.
- W4297790983 hasPublicationYear "2020" @default.
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