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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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On the Limitations of Unsupervised Bilingual Dictionary Induction ...
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Scoring Lexical Entailment with a Supervised Directional Similarity Network ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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Abstract:
Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language ... : This work is supported by the ERC Consolidator Grant LEXICAL (648909) ...
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URL: https://dx.doi.org/10.17863/cam.27304 https://www.repository.cam.ac.uk/handle/1810/279936
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A deep learning approach to bilingual lexicon induction in the biomedical domain ...
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
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