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Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo; Naradowsky, Jason; Reichart, Roi; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018
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) ...
URL: https://dx.doi.org/10.17863/cam.27304
https://www.repository.cam.ac.uk/handle/1810/279936
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : MIT Press - Journals, 2018. : Transactions of the Association for Computational Linguistics, 2018
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