Review — Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Feedforward Neural Network for Word Prediction
In this story, Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model, by Université de Montréal, is reviewed. This is a paper by Prof. Yoshua Bengio. In this paper:
- A feedforward neural network is trained to approximate probabilities over sequences of words.
- Adaptive importance sampling is designed to accelerate the training.
This is a paper in 2007 TNN with over 200 citations, where TNN has become TNNLS in 2011, and TNNLS has high impact factor of 10.451. (Sik-Ho Tsang @ Medium) Though this paper mainly targets for predicting the next word, word prediction is the foundation to build a language model.
- Neural Language Model Architecture
- Adaptive Importance Sampling
- Experimental Results
1. Neural Language Model Architecture
- Basically, the network we may think is simple if we compare with the current SOTA approach. Yet, it is amazing at that moment.
- For the existing words wt-1 to wt-n+1, they are transformed to zi using the shared weight C.
- For the next word that needs to predict, a separate D is used to transform it to z0.
- Then, a hidden layer of W (weights) with d (bias) is used with tanh activation to transform z to a:
- Finally, the output is a scalar energy function:
- where bwt is bias and Vwt is the weight from hidden layer to output layer. To obtain the probability:
- i.e. the softmax operation.
2. Adaptive Importance Sampling
2.1. Classical Monte Carlo
- At that moment, conventionally, classical Monte Carlo was used to estimate the gradient of the log-likelihood:
2.2. Biased Importance Sampling
- In this paper, Biased Importance Sampling is proposed:
- where a multiplicative constant w is used.
- Thus, the gradient updated is scaled.
- which is similar to nowadays weight update procedure.
2.3. Effective Sample Size (ESS)
- That is similar to the minibatch size nowadays but ESS is adaptive to w:
3. Experimental Results
- Brown corpus dataset is used.
- The Brown corpus consists of 1,181,041 words from various American English documents.
- The corpus was divided in train (800,000 words), validation (200,000 words), and test (the remaining 180,000 words) sets.
- The vocabulary was truncated by mapping all “rare” words (words that appear three times or less in the corpus) into a single special word.
- The resulting vocabulary contains 14,847 words.
- A simple interpolated trigram, serving as baseline, achieves a perplexity of 253.8 on the test set.
- The figure shows that the convergence of both networks is similar. The same holds for validation and test errors.
- The network trained by sampling converges to an even lower perplexity than the ordinary one (trained with the exact gradient).
After 9 epochs (26h), its perplexity over the test set is equivalent to that of the one trained with exact gradient at its overfitting point (18 epochs, 113 days).
- Surprisingly enough, if letting the sampling-trained model converge, it starts to overfit at epoch 18 — as for classical training — but with a lower test perplexity of 196.6, a 3.8% improvement.
- Total improvement in test perplexity with respect to the trigram baseline is 29%.
- The required number of samples with the non-adaptive unigram was growing exponentially.
[2007 TNN] [Bengio TNN’07]
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Natural Language Processing (NLP)
Language Model: 2007 [Bengio TNN’07]
Machine Translation: 2014 [Seq2Seq] [RNN Encoder-Decoder] 2015 [Attention Decoder/RNNSearch]
Image Captioning: 2015 [m-RNN] [R-CNN+BRNN] [Show and Tell/NIC]