Structured OUtput Layer (SOUL) Neural Network Language Model

Transcription

1 Structured OUtput Layer (SOUL) Neural Network Language Model Le Hai Son, Ilya Oparin, Alexandre Allauzen, Jean-Luc Gauvain, François Yvon 25/5/211 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 1 / 22

2 Outline 1 Neural Network Language Models 2 Hierarchical Models 3 SOUL Neural Network Language Model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 2 / 22

3 Plan Neural Network Language Models 1 Neural Network Language Models 2 Hierarchical Models 3 SOUL Neural Network Language Model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 3 / 22

4 N-gram models Neural Network Language Models Very successful but sparsity issues and lack of generalization Flat vocabulary Each word is only a possible outcome of a discrete random variable, an index in the vocabulary L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 4 / 22

5 Neural Network Language Models Estimate n-gram probabilities in a continuous space NNLMs were introduced in [Bengio et al., 21] and applied to speech recognition in [Schwenk and Gauvain, 22]. hy should it work? similar words are expected to have similar feature vectors Probability function is a smooth function of feature values A small change in features will induce a small change in the probability L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 5 / 22

6 Neural Network Language Models Project a word sequence in a continuous space epresent words as as 1-of- V vectors Project the word in the continuous space: add a second layer fully connected For a 4-gram, the history is a sequence of 3 words Merge these three vectors to derive a single vector for the history w 1 V : vocabulary size A neuron layer represents a vector of values, one neuron per value L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 6 / 22

7 Neural Network Language Models Project a word sequence in a continuous space epresent words as as 1-of- V vectors Project the word in the continuous space: add a second layer fully connected For a 4-gram, the history is a sequence of 3 words Merge these three vectors to derive a single vector for the history w 1 The connection between two layers is a matrix operation The matrix contains all the connection weights v is a continuous vector v L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 6 / 22

8 Neural Network Language Models Project a word sequence in a continuous space shared projection space epresent words as as 1-of- V vectors Project the word in the continuous space: add a second layer fully connected For a 4-gram, the history is a sequence of 3 words Merge these three vectors to derive a single vector for the history w i-1 w i-2 w i v i-1 v i-2 v i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 6 / 22

9 Neural Network Language Models Project a word sequence in a continuous space shared projection space epresent words as as 1-of- V vectors Project the word in the continuous space: add a second layer fully connected For a 4-gram, the history is a sequence of 3 words Merge these three vectors to derive a single vector for the history w i v i-1 v i-2 w i-2 w i-3 1 v i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 6 / 22

10 Neural Network Language Models Estimate the n-gram probability shared projection space w i-1 Given the history expressed as a feature vector Create a feature vector for the word to be predicted in the prediction space Estimate probabilities for all words given the history All the parameters must be learned (, ih, ho ). w i-2 context layer ih ho w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 7 / 22

11 Neural Network Language Models Estimate the n-gram probability shared projection space Given the history expressed as a feature vector Create a feature vector for the word to be predicted in the prediction space Estimate probabilities for all words given the history All the parameters must be learned (, ih, ho ). w i-1 w i-2 hidden layer: tanh activation ih ho w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 7 / 22

12 Neural Network Language Models Estimate the n-gram probability shared projection space w i-1 prediction space Given the history expressed as a feature vector Create a feature vector for the word to be predicted in the prediction space Estimate probabilities for all words given the history All the parameters must be learned (, ih, ho ). w i-2 ih output layer (softmax) ho w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 7 / 22

13 Neural Network Language Models Estimate the n-gram probability shared projection space w i-1 prediction space Given the history expressed as a feature vector Create a feature vector for the word to be predicted in the prediction space Estimate probabilities for all words given the history All the parameters must be learned (, ih, ho ). w i-2 ih output layer (softmax) ho w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 7 / 22

14 Early assessment Neural Network Language Models Key points The projection in continuous spaces reduces the sparsity issues Learn simultaneously the projection and the prediction w i-1 Probability estimation based on the similarity among the feature vectors In practice ih ho Significant and systematic improvements In machine translation and speech recognition tasks w i-2 w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 8 / 22

15 Early assessment Neural Network Language Models Key points The projection in continuous spaces reduces the sparsity issues Learn simultaneously the projection and the prediction w i-1 Probability estimation based on the similarity among the feature vectors In practice ih ho Significant and systematic improvements In machine translation and speech recognition tasks w i-2 w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 8 / 22

16 Early assessment Neural Network Language Models Key points The projection in continuous spaces reduces the sparsity issues Learn simultaneously the projection and the prediction w i-1 Probability estimation based on the similarity among the feature vectors In practice Significant and systematic improvements In machine translation and speech recognition tasks Everybody should use it! w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 8 / 22

17 Neural Network Language Models Early assessment Key points The projection in continuous spaces reduces the sparsity issues Learn simultaneously the projection and the prediction ith a small training set In practice Significant and systematic improvements In machine translation and speech recognition tasks Learning and inference time L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 8 / 22

18 Neural Network Language Models Early assessment Key points The projection in continuous spaces reduces the sparsity issues Learn simultaneously the projection and the prediction ith a large training set In practice Significant and systematic improvements In machine translation and speech recognition tasks Learning and inference time L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 8 / 22

19 Neural Network Language Models hy it is so long? - Inference Forward propagation of the history The projection: select a row in Compute a vector for the predicted word Estimate the probability for all the words V w i-1 Matrix row selection Complexity issues The input vocabulary can be as large as we want Increasing the order does not drastically increase the complexity The problem is the output vocabulary size w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 9 / 22

20 Neural Network Language Models hy it is so long? - Inference Forward propagation of the history The projection: select a row in Compute a vector for the predicted word Estimate the probability for all the words V w i-1 Matrix multiplication 6x2 Complexity issues The input vocabulary can be as large as we want Increasing the order does not drastically increase the complexity The problem is the output vocabulary size w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 9 / 22

21 Neural Network Language Models hy it is so long? - Inference Forward propagation of the history The projection: select a row in Compute a vector for the predicted word Estimate the probability for all the words V w i-1 Matrix multiplication 2 x V Complexity issues The input vocabulary can be as large as we want Increasing the order does not drastically increase the complexity The problem is the output vocabulary size w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 9 / 22

22 Neural Network Language Models hy it is so long? - Inference Forward propagation of the history The projection: select a row in Compute a vector for the predicted word Estimate the probability for all the words V w i-1 Matrix multiplication 2 x V Complexity issues The input vocabulary can be as large as we want Increasing the order does not drastically increase the complexity The problem is the output vocabulary size w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 9 / 22

23 Neural Network Language Models hy it is so long? - Inference Forward propagation of the history The projection: select a row in Compute a vector for the predicted word Estimate the probability for all the words V w i-1 Matrix multiplication 2 x V Complexity issues The input vocabulary can be as large as we want Increasing the order does not drastically increase the complexity The problem is the output vocabulary size w i-2 w i-3 ih ho L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 9 / 22

24 Neural Network Language Models Usual tricks to speed-up training (and inference) e-sampling and batch training For each epoch: down-sampling of the training data Forward and Back-propagation for a group of n-grams educe the output vocabulary Use the Neural network to predict only the K most frequent words For a tractable model: K = 6 to 2 equires the normalization of the distribution for the whole vocabulary use the standard n-gram LM L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 1 / 22

25 Plan Hierarchical Models 1 Neural Network Language Models 2 Hierarchical Models 3 SOUL Neural Network Language Model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

26 Hierarchical Models Speeding up MaxEnt models Main ideas as proposed in [Goodman, 21] Instead of computing directly P(w h), make use of clustering of words into classes: P(w h) = P(w c(w), h)p(c(w) h) Any classes can be used, but generalization may be better for classes for which it s easier to learn P(c(w) h) Example of reduction 1 word vocabulary with 1 classes 2 normalizations over 1 outcomes 1 2 (reduction by 5) L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

27 Hierarchical Models Hierarchical Probabilistic NNLM Main ideas as proposed in [Morin and Bengio, 25] Perform binary hierarchical clustering of the vocabulary Predict words as paths in this clustering tree Details Clustering is constrained by ordnet semantic hierarchy Predicting next bit in hierarchy as P(b node, w t 1,..., w t n+1 ) esults Brown corpus, 1M words, 1 words vocabulary Speed-up but loss in perplexity as compared to a standard NNLM L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

28 Hierarchical Models Scalable Hierarchical Distributed LM Main ideas as proposed in [Mnih and Hinton, 28] Use automatic clustering instead of ordnet Implement as log-bilinear model One-to-many word class mapping esults APNews dataset, 14M words, 18k vocabulary Perplexity improvements over n-gram model, similar performance to a non-hierarchical LBL No comparison with non-linear NNLMs used in STT L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

29 Plan SOUL Neural Network Language Model 1 Neural Network Language Models 2 Hierarchical Models 3 SOUL Neural Network Language Model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

30 SOUL Neural Network Language Model Structured OUtput Layer NNLM Main ideas Trees are not binary Multiple output layers with a softmax in each No clustering for frequent words Compromise between speed and complexity Efficient clustering scheme ord vectors in projection space are used for clustering Task Improving state-of-the-art STT system that makes use of shortlist NNLMs Large vocabulary and the baseline n-gram LM trained on billions of words L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

31 SOUL Neural Network Language Model ord clustering Associate each frequent word with a single class c 1 (w) Split other words in sub-classes (c 2 (w)) and so on L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

32 SOUL Neural Network Language Model ord clustering Associate each frequent word with a single class c 1 (w) Split other words in sub-classes (c 2 (w)) and so on L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

33 SOUL Neural Network Language Model ord clustering Associate each frequent word with a single class c 1 (w) Split other words in sub-classes (c 2 (w)) and so on C 1 (w) L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

34 SOUL Neural Network Language Model ord clustering Associate each frequent word with a single class c 1 (w) Split other words in sub-classes (c 2 (w)) and so on C 1 (w) C 2 (w) L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

35 SOUL Neural Network Language Model ord clustering Associate each frequent word with a single class c 1 (w) Split other words in sub-classes (c 2 (w)) and so on C 1 (w) C 2 (w) C 3 (w) L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

36 SOUL Neural Network Language Model ord probability C 1 (w) C 2 (w) C 3 (w) D P(w i h) = P(c 1 (w i ) h) P(c d (w i ) h, c 1:d 1 ) c 1:D (w i ) = c 1,..., c D : path for the word w i in the clustering tree, D : depth of the tree, c d (w i ): (sub-)class, c D (w i ): leaf d=2 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

37 SOUL Neural Network Language Model The SOUL language model w i-1 C 1 (w) w i-2 ih ho w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

38 SOUL Neural Network Language Model The SOUL language model w i-1 ih w i-2 w i-3 L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

39 Training algorithm SOUL Neural Network Language Model Step 1: Train a standard NNLM model with the shortlist as an output (3 epochs and a shortlist of 8k words) Step 2: educe the dimension of the context space using with PCA (final dimension is 1 in our experiments) Step 3: Perform a recursive K -means word clustering based on the distributed representation induced by the continuous space (except for words in the shortlist) Step 4: Train the whole model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 2 / 22

40 Training algorithm SOUL Neural Network Language Model Step 1: Train a standard NNLM model with the shortlist as an output (3 epochs and a shortlist of 8k words) Step 2: educe the dimension of the context space using with PCA (final dimension is 1 in our experiments) Step 3: Perform a recursive K -means word clustering based on the distributed representation induced by the continuous space (except for words in the shortlist) Step 4: Train the whole model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 2 / 22

41 Training algorithm SOUL Neural Network Language Model Step 1: Train a standard NNLM model with the shortlist as an output (3 epochs and a shortlist of 8k words) Step 2: educe the dimension of the context space using with PCA (final dimension is 1 in our experiments) Step 3: Perform a recursive K -means word clustering based on the distributed representation induced by the continuous space (except for words in the shortlist) Step 4: Train the whole model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 2 / 22

42 Training algorithm SOUL Neural Network Language Model Step 1: Train a standard NNLM model with the shortlist as an output (3 epochs and a shortlist of 8k words) Step 2: educe the dimension of the context space using with PCA (final dimension is 1 in our experiments) Step 3: Perform a recursive K -means word clustering based on the distributed representation induced by the continuous space (except for words in the shortlist) Step 4: Train the whole model L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/211 2 / 22

43 SOUL Neural Network Language Model STT results with SOUL NNLMs Mandarin GALE task LIMSI Mandarin STT system 56k vocabulary Baseline LM trained on 3.2 billion words 4 NNLMs trained on 25M words after resampling model ppx CE dev9 dev9s eval9 Baseline 4-gram % 8.9% +4-gram NNLM 8k % 8.6% +4-gram NNLM 12k % 8.6% +4-gram SOUL NNLM % 8.5% +6-gram NNLM 8k % 8.5% +6-gram NNLM 12k % 8.5% +6-gram SOUL NNLM % 8.3% L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

44 SOUL Neural Network Language Model Conclusion Neural network and class-based language models combined together SOUL LM is able to deal with vocabularies of arbitrary sizes Speech recognition improvements are achieved on a large-scale task and over challenging baselines SOUL LM improves better for longer contexts L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22

45 SOUL Neural Network Language Model Bengio, Y., Ducharme,., and Vincent, P. (21). A neural probabilistic language model. Neural Information Processing Systems, 13: Goodman, J. (21). Classes for fast maximum entropy training. In Proc. of ICASSP 1. Mnih, A. and Hinton, G. (28). A scalable hierarchical distributed language model. In Neural Information Processing Systems, volume 21, pages Morin, F. and Bengio, Y. (25). Hierarchical probabilistic neural network language model. In Proc. of AISTATS 5, pages Schwenk, H. and Gauvain, J.-L. (22). Connectionist language modeling for large vocabulary continuous speech recognition. In Proc. of ICASSP 2, pages L.-H. Son, I. Oparin et al. (LIMSI-CNS) SOUL NNLM 25/5/ / 22