文献紹介：Recurrent Neural Network based Language Model

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
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   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
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4. 2. Model Description
   • Simple Recurrent Neural Network
   • Optimization
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5. 2.1 Simple Recurrent Neural
   Network
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6. 2.1 Simple Recurrent Neural
   Network
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   • 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
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   • Word-probabilities:
   

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8. 3. Experiments
   • Wall Street Journal (WSJ) Experiments
   • NIST Rich Transcription Evaluation 2005 (RT05)
   Experiments
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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
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10. 3.1 WSJ Experiments
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11. 3.1 WSJ Experiments
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12. 3.1 WSJ Experiments
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13. 3. NIST RT05 Experiments
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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
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    Vietnamese Morphological Analysis
    2017/05/17
    

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