Neural network-based language models deal with data sparsity problems by mapping the large discrete space of words into a smaller continuous space of real-valued vectors. By learning distributed vector representations for words, each training sample informs the neural network model about a combinatorial number of other patterns. In this paper, we exploit the sparsity in natural language even further by encoding each unique input word using a fixed sparse random representation. These sparse codes are then projected onto a smaller embedding space which allows for the encoding of word occurrences from a possibly unknown vocabulary, along with the creation of more compact language models using a reduced number of parameters. We investigate the properties of our encoding mechanism empirically, by evaluating its performance on the widely used Penn Treebank corpus. We show that guaranteeing approximately equidistant (nearly orthogonal) vector representations for unique discrete inputs is enough to provide the neural network model with enough information to learn --and make use-- of distributed representations for these inputs.
In this paper, we focus on exploring simple feedforward neural network architectures for multiple reasons. First, using incrementally complex models allows us to determine which architectural components allow neural network models to deal with random-projection encoding. The work from in energy-based neural language models @cite_20 @cite_26 is a good starting point for our baseline because the energy-based model gives us a way to translate random vector predictions into word probability distributions.
@cite_26 @cite_20
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We show how to improve a state-of-the-art neural network language model that converts the previous ''context'' words into feature vectors and combines these feature vectors linearly to predict the feature vector of the next word. Significant improvements in predictive accuracy are achieved by using a non-linear subnetwork to modulate the effects of the context words or to produce a non-linear correction term when predicting the feature vector. A log-bilinear language model that incorporates both of these improvements achieves a 26 reduction in perplexity over the best n-gram model on a fairly large dataset. The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficulties caused by data sparsity. We propose three new probabilistic language models that define the distribution of the next word in a sequence given several preceding words by using distributed representations of those words. We show how real-valued distributed representations for words can be learned at the same time as learning a large set of stochastic binary hidden features that are used to predict the distributed representation of the next word from previous distributed representations. Adding connections from the previous states of the binary hidden features improves performance as does adding direct connections between the real-valued distributed representations. One of our models significantly outperforms the very best n-gram models.