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1
Data-to-text generation with neural planning
Puduppully, Ratish Surendran. - : The University of Edinburgh, 2022
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2
Intrinsic Bias Metrics Do Not Correlate with Application Bias ...
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3
Training dynamics of neural language models
Saphra, Naomi. - : The University of Edinburgh, 2021
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4
Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals ...
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5
Understanding and generating language with abstract meaning representation
Damonte, Marco. - : The University of Edinburgh, 2020
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6
On understanding character-level models for representing morphology
Vania, Clara. - : The University of Edinburgh, 2020
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7
Methods for morphology learning in low(er)-resource scenarios
Bergmanis, Toms. - : The University of Edinburgh, 2020
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8
Modelling speaker adaptation in second language learner dialogue
Sinclair, Arabella Jane. - : The University of Edinburgh, 2020
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9
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars ...
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10
Lifecycle of neural semantic parsing
Cheng, Jianpeng. - : The University of Edinburgh, 2019
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11
Fast machine translation on parallel and massively parallel hardware
Bogoychev, Nikolay Veselinov. - : The University of Edinburgh, 2019
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12
Learning natural language interfaces with neural models
Dong, Li. - : The University of Edinburgh, 2019
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13
Probabilistic graph formalisms for meaning representations
Gilroy, Sorcha. - : The University of Edinburgh, 2019
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14
Low-resource speech translation
Bansal, Sameer. - : The University of Edinburgh, 2019
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15
Indicatements that character language models learn English morpho-syntactic units and regularities ...
Abstract: Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1809.00066
https://dx.doi.org/10.48550/arxiv.1809.00066
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16
Neural Networks for Cross-lingual Negation Scope Detection ...
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17
Understanding Learning Dynamics Of Language Models with SVCCA ...
Saphra, Naomi; Lopez, Adam. - : arXiv, 2018
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18
Entity-based coherence in statistical machine translation: a modelling and evaluation perspective
Wetzel, Dominikus Emanuel. - : The University of Edinburgh, 2018
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19
Computational models for multilingual negation scope detection
Fancellu, Federico. - : The University of Edinburgh, 2018
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20
CoNLL 2017 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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