A Survey on Context Learning <s> Incorporating Historical Context with RNN <s> Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. <s> BIB001 </s> A Survey on Context Learning <s> Incorporating Historical Context with RNN <s> A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition <s> BIB002
Despite the improvement of fast training, a drawback of the above neural network based models is that only a small amount of context words are utilized during training, and, as with the n-gram model, the size of the context window is not very big (usually smaller than 20). In order to incorporate longer-ranged historical context information and further reduce the language model perplexity, BIB002 proposes the recurrent neural network based language model (RNNLM) which is based on the so-called a simple recurrent neural network BIB001 , as shown in Fig. 7 . The RNNLM model consists of three layers: input layer x, hidden layer s (also called context layer or state) and output layer y. Input vector xðtÞ is a concatenation of vector wðtÞ representing the current word and the output vector of the hidden layer from the previous time stamp sðt À 1Þ. Then the hidden and output layers are computed as follows: where fðzÞ is the sigmoid activation function and gðzÞ is the softmax function to guarantee probabilities on the output layer summing up to 1. The RNNLM model can be trained by a standard backpropagation algorithm with stochastic gradient descent. sð0Þ can be set to a vector of small values for initialization. This model achieves one of the largest perplexity reduction ever reported-45.2 percent compared with Kneser-Ney five-gram.