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In this paper, we study language models based on recurrent neural networks on three databases in two languages. We implement basic recurrent neural networks (RNN) and refined RNNs with long short-term memory (LSTM) cells. We use the corpora of Penn Tree Bank (PTB) and AMI in English, and the Academia Sinica Balanced Corpus (ASBC) in Chinese. On ASBC, we investigate word-based and character-based language...
It has been argued that recurrent neural network language models are better in capturing long-range dependency than n-gram language models. In this paper, we attempt to verify this claim by investigating the prediction accuracy and the perplexity of these language models as a function of word position, i.e., the position of a word in a sentence. It is expected that as word position increases, the...
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