We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.
@cite_5 is inspired by the idea that we could use convolution network to produce word embedding from a character level model. Aside from a big reduction in number of parameters and incorporating morphological knowledge from words, this method can also easily deal with out-of-vocabulary words, and allows parallel training over corpora that have different vocabulary size. But this method does not decrease the computational complexity compared to the standard full softmax @cite_5 .
@cite_5
2963012544
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.