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Learning with joint inference and latent linguistic structure in graphical models ...
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Learning with joint inference and latent linguistic structure in graphical models ...
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Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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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 modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available. ; This work is supported by the ERC Consolidator Grant LEXICAL (648909)
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URL: https://www.repository.cam.ac.uk/handle/1810/279936 https://doi.org/10.17863/CAM.27304
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A Structured Variational Autoencoder for Contextual Morphological Inflection
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Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources ...
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Learning with joint inference and latent linguistic structure in graphical models
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