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1
Learning English with Peppa Pig ...
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2
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations ...
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3
Discrete representations in neural models of spoken language ...
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4
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling ...
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5
Analyzing analytical methods: The case of phonology in neural models of spoken language ...
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6
Learning to Understand Child-directed and Adult-directed Speech ...
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7
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning ...
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8
On the difficulty of a distributional semantics of spoken language
In: Proceedings of the Society for Computation in Linguistics (2019)
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9
Lessons learned in multilingual grounded language learning ...
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10
Revisiting the Hierarchical Multiscale LSTM ...
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11
On the difficulty of a distributional semantics of spoken language ...
Abstract: In the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units. In contrast, for written language, where there is a large body of work on unsupervised induction of semantic representations of words, whole sentences and longer texts. In this study we examine the challenges of adapting these approaches from written to spoken language. We conjecture that unsupervised learning of the semantics of spoken language becomes feasible if we abstract from the surface variability. We simulate this setting with a dataset of utterances spoken by a realistic but uniform synthetic voice. We evaluate two simple unsupervised models which, to varying degrees of success, learn semantic representations of speech fragments. Finally we present inconclusive results on human speech, and discuss the challenges inherent in learning distributional semantic representations on unrestricted natural spoken language. ... : Proceedings of the Society for Computation in Linguistics 2019 ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.1803.08869
https://arxiv.org/abs/1803.08869
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12
Encoding of phonology in a recurrent neural model of grounded speech ...
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13
Rnn Models For Representation Of Linguistic Form And Function In Recurrent Neural Networks ...
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14
Rnn Models For Representation Of Linguistic Form And Function In Recurrent Neural Networks ...
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15
Representations of language in a model of visually grounded speech signal ...
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16
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning ...
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17
Representation of linguistic form and function in recurrent neural networks ...
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18
Elephant: Sequence Labeling for Word and Sentence Segmentation
In: EMNLP 2013 ; https://hal.archives-ouvertes.fr/hal-01344500 ; EMNLP 2013, Oct 2013, Seattle, United States (2013)
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19
Text segmentation with character-level text embeddings ...
Chrupała, Grzegorz. - : arXiv, 2013
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20
Elephant: Sequence labeling for word and sentence segmentation
Evang, Kilian; Basile, Valerio; Chrupała, Grzegorz. - : Association for Computational Linguistics (ACL), 2013. : country:USA, 2013. : place:Stroudsburg, 2013
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