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Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks ...
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Putting words in context: LSTM language models and lexical ambiguity ...
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Towards incremental learning of word embeddings using context informativeness
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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics : Student Research Workshop pp. 162-168 (2019)
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Colorless green recurrent networks dream hierarchically
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Word order variation and dependency length minimisation : a cross-linguistic computational approach ...
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Word order variation and dependency length minimisation : a cross-linguistic computational approach
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Colorless green recurrent networks dream hierarchically
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In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018 P. 1195–1205 (2018)
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Discontinuous Verb Phrases in Parsing and Machine Translation of English and German
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In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (2016)
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Multi-lingual Dependency Parsing Evaluation : a Large-scale Analysis of Word Order Properties using Artificial Data
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In: ISSN: 2307-387X ; Transactions of the Association for Computational Linguistics, Vol. 4 (2016) pp. 343-356 (2016)
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Dependency length minimisation effects in short spans: a large-scale analysis of adjective placement in complex noun phrases
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In: ACL 2015 - the 53rd annual meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01174617 ; ACL 2015 - the 53rd annual meeting of the Association for Computational Linguistics, Jul 2015, Beijing, China (2015)
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Deep daxes: mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
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Putting words in context: LSTM language models and lexical ambiguity
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How to represent a word and predict it, too: improving tied architectures for language modelling
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How to represent a word and predict it, too: improving tied architectures for language modelling
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