Recurrent neural network based language model

Tomas Mikolov, Martin Karafiat, Lukas Burget, JanCernocky, and Sanjeev Khudanpur. Recurrent neural network based language model. In 11th Annual Conference of the International Speech Communication Association, pp.1045–1048, 2010.

written by Nguyen Van Hai.

Transcript

1. 文献紹介 平成29年5月23日（火）
   Recurrent Neural Network
   based Language Model
   長岡技術科学大学
   自然言語処理研究室 修士2年
   NGUYEN VAN HAI
   

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2. Information
   2
   Tomas Mikolov, Martin Karafiat, Lukas Burget,
   JanCernocky, and Sanjeev Khudanpur
   Recurrent neural network based language model
   In 11th Annual Conference of the International
   Speech Communication Association, pp.1045–
   1048, 2010
   

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3. 1. Introduction
   • Statistical language modeling:
   • Predict the next word in textual data
   • Special language domain:
   • Sentence must be described by parse trees
   • Morphology of words, syntax and semantics
   • There are some significant progress in language model
   • Measure by ability of models to better predict sequential data
   3
   

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4. 2. Model Description
   • Simple Recurrent Neural Network
   • Optimization
   4
   

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5. 2.1 Simple Recurrent Neural
   Network
   5
   

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6. 2.1 Simple Recurrent Neural
   Network
   6
   • Networks are trained in several epochs
   • Weights are initialized to small values
   • Train network by standard backpropagation
   algorithm with stochastic gradient descent
   • Error vector:
   

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7. 2.2 Optimization
   7
   • Word-probabilities:
   

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8. 3. Experiments
   • Wall Street Journal (WSJ) Experiments
   • NIST Rich Transcription Evaluation 2005 (RT05)
   Experiments
   8
   

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9. 3.1 WSJ Experiments
   • Training corpus
   • 37M words from NYT section of English Gigaword
   • Training 6.4M words (300K sentences)
   • Perplexity evaluated on 230K words
   • Kneser-Ney smoothed 5-gram as KN5
   • RNN 90/2
   • Hidden layer size is 90
   • Threshold for merging words to rare token is 2
   9
   

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10. 3.1 WSJ Experiments
    10
    

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11. 3.1 WSJ Experiments
    11
    

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12. 3.1 WSJ Experiments
    12
    

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13. 3. NIST RT05 Experiments
    13
    

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14. Conclusion and future work
    • In WSJ, WER
    • Around 18% with the same data
    • Around 12% when backoff model is trained with
    data 5 times than RNN model
    • NIST RT05 can outperform big backoff models
    14
    Vietnamese Morphological Analysis
    2017/05/17
    

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