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        <title>Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models</title>
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        <namePart type="given">Yiben</namePart>
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    <abstract>Recurrent neural network language models (RNNLM) form a valuable foundation for many NLP systems, but training the models can be computationally expensive, and may take days to train on a large corpus. We explore a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus. In experiments with the Billion-Word and Wikitext corpora, we show that the technique is effective, and more time-efficient than simply training on a larger sequential corpus. We also introduce new strategies for selecting the most informative n-grams, and show that these boost efficiency.</abstract>
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%0 Conference Proceedings
%T Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models
%A Yang, Yiben
%A Wang, Ji-Ping
%A Downey, Doug
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F yang-etal-2019-using
%X Recurrent neural network language models (RNNLM) form a valuable foundation for many NLP systems, but training the models can be computationally expensive, and may take days to train on a large corpus. We explore a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus. In experiments with the Billion-Word and Wikitext corpora, we show that the technique is effective, and more time-efficient than simply training on a larger sequential corpus. We also introduce new strategies for selecting the most informative n-grams, and show that these boost efficiency.
%R 10.18653/v1/N19-1330
%U https://aclanthology.org/N19-1330
%U https://doi.org/10.18653/v1/N19-1330
%P 3268-3273

Markdown (Informal)

[Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models](https://aclanthology.org/N19-1330) (Yang et al., NAACL 2019)

• Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models (Yang et al., NAACL 2019)

ACL

• Yiben Yang, Ji-Ping Wang, and Doug Downey. 2019. Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3268–3273, Minneapolis, Minnesota. Association for Computational Linguistics.