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Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale ...
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Diverse Pretrained Context Encodings Improve Document Translation ...
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Better {C}hinese Sentence Segmentation with Reinforcement Learning ...
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Better Document-Level Machine Translation with Bayes’ Rule
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 346-360 (2020) (2020)
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Unsupervised Bilingual POS Tagging with Markov Random Fields ...
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Unsupervised Bilingual POS Tagging with Markov Random Fields ...
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Knowledge-Rich Morphological Priors for Bayesian Language Models ...
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Learning to Discover, Ground and Use Words with Segmental Neural Language Models ...
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From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042)
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Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling ...
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From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042) ...
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Ontology-Aware Token Embeddings for Prepositional Phrase Attachment ...
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Massively Multilingual Word Embeddings ...
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Abstract:
We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1602.01925 https://arxiv.org/abs/1602.01925
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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
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