What directions are suggested to improve language models?
Improved architecture for ANN, use of linguistical properties of words or sentences as features.
An exhaustive study on neural network language modeling (NNLM) is performed in this paper. Different architectures of basic neural network language models are described and examined. A number of different improvements over basic neural network language models, including importance sampling, word classes, caching and bidirectional recurrent neural network (BiRNN), are studied separately, and the advantages and disadvantages of every technique are evaluated. Then, the limits of neural network language modeling are explored from the aspects of model architecture and knowledge representation. Part of the statistical information from a word sequence will loss when it is processed word by word in a certain order, and the mechanism of training neural network by updating weight matrixes and vectors imposes severe restrictions on any significant enhancement of NNLM. For knowledge representation, the knowledge represented by neural network language models is the approximate probabilistic distribution of word sequences from a certain training data set rather than the knowledge of a language itself or the information conveyed by word sequences in a natural language. Finally, some directions for improving neural network language modeling further is discussed.
Generally, a well-designed language model makes a critical difference in various natural language processing (NLP) tasks, like speech recognition BIBREF0 , BIBREF1 , machine translation BIBREF2 , BIBREF3 , semantic extraction BIBREF4 , BIBREF5 and etc. Language modeling (LM), therefore, has been the research focus in NLP field all the time, and a large number of sound research results have been published in the past decades. N-gram based LM BIBREF6 , a non-parametric approach, is used to be state of the art, but now a parametric method - neural network language modeling (NNLM) is considered to show better performance and more potential over other LM techniques, and becomes the most commonly used LM technique in multiple NLP tasks. Although some previous attempts BIBREF7 , BIBREF8 , BIBREF9 had been made to introduce artificial neural network (ANN) into LM, NNLM began to attract researches' attentions only after BIBREF10 and did not show prominent advantages over other techniques of LM until recurrent neural network (RNN) was investigated for NNLM BIBREF11 , BIBREF12 . After more than a decade's research, numerous improvements, marginal or critical, over basic NNLM have been proposed. However, the existing experimental results of these techniques are not comparable because they were obtained under different experimental setups and, sometimes, these techniques were evaluated combined with other different techniques. Another significant problem is that most researchers focus on achieving a state of the art language model, but the limits of NNLM are rarely studied. In a few works BIBREF13 on exploring the limits of NNLM, only some practical issues, like computational complexity, corpus, vocabulary size, and etc., were dealt with, and no attention was spared on the effectiveness of modeling a natural language using NNLM. Since this study focuses on NNLM itself and does not aim at raising a state of the art language model, the techniques of combining neural network language models with other kind of language models, like N-gram based language models, maximum entropy (ME) language models and etc., will not be included. The rest of this paper is organized as follows: In next section, the basic neural network language models - feed-forward neural network language model (FNNLM), recurrent neural network language model (RNNLM) and long-short term memory (LSTM) RNNLM, will be introduced, including the training and evaluation of these models. In the third section, the details of some important NNLM techniques, including importance sampling, word classes, caching and bidirectional recurrent neural network (BiRNN), will be described, and experiments will be performed on them to examine their advantages and disadvantages separately. The limits of NNLM, mainly about the aspects of model architecture and knowledge representation, will be explored in the fourth section. A further work section will also be given to represent some further researches on NNLM. In last section, a conclusion about the findings in this paper will be made.