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• W2020757772 abstract "In this paper, we propose the use of distance and co-occurrence information of word-pairs to improve language modeling. We have empirically shown that, for history-context sizes of up to ten words, the extracted information about distance and co-occurrence complements the n-gram language model well, for which learning long-history contexts is inherently difficult. Evaluated on the Wall Street Journal and the Switchboard corpora, our proposed model reduces the trigram model perplexity by up to 11.2% and 6.5%, respectively. As compared to the distant bigram model and the trigger model, our proposed model offers a more effective manner of capturing far context information, as verified in terms of perplexity and computational efficiency, i.e., fewer free parameters to be fine-tuned. Experiments using the proposed model for speech recognition, text classification and word prediction tasks showed improved performance." @default.
• W2020757772 created "2016-06-24" @default.
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• W2020757772 date "2015-07-01" @default.
• W2020757772 modified "2023-09-25" @default.
• W2020757772 title "Decoupling Word-Pair Distance and Co-occurrence Information for Effective Long History Context Language Modeling" @default.
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• W2020757772 doi "https://doi.org/10.1109/taslp.2015.2425223" @default.
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