DOCSLIB.ORG

A Dynamic Language Model for Speech Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Dynamic Language Model for Speech Recognition A Dynamic Language Model for Speech Recognition F. Jelinek, B. Merialdo, S. Roukos, and M. Strauss I IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY 10598 ABSTRACT eral words are bursty by nature, i.e., one expects the In the case of a trlgr~m language model, the proba- word "language" to occur in this paper at a signif- bility of the next word conditioned on the previous two icantly higher rate than the average frequency esti- words is estimated from a large corpus of text. The re- mated from a large collection of text. To capture the sulting static trigram language model (STLM) has fixed "dynamic" nature of the trigram probabilities in a probabilities that are independent of the document being particular document, we present a "cache;' trigram dictated. To improve the language mode] (LM), one can language model (CTLM) that uses a window of the n adapt the probabilities of the trigram language model to match the current document more closely. The partially most recent words to determine the probability dis- dictated document provides significant clues about what tribution of the next word. words ~re more likely to be used next. Of many meth- ods that can be used to adapt the LM, we describe in this The idea of using a window of the recent history to paper a simple model based on the trigram frequencies es- adjust the LM probabilities was proposed in [2, 3]. In timated from the partially dictated document. We call this [2] tile dynamic component adjusted the conditional model ~ cache trigram language model (CTLM) since we probability, p,(w,+l ] g,+l), of the next word, wn+l, are c~chlng the recent history of words. We have found given a predicted part-oPspeech (POS), g,~+l, in a tri- that the CTLM red,aces the perplexity of a dictated doc- ument by 23%. The error rate of a 20,000-word isolated part-of-speech language model. Each POS had a sep- word recognizer decreases by about 5% at the beginning of arate cache where the frequencies of all the words that a document and by about 24% after a few hundred words. occurred with a POS determine the dynamic compo- nent of the language model. As a word is observed it is tagged and the appropriate POS cache is updated. At least 5 occurrences per cache are required before INTRODUCTION activating it. P~eliminary results for a couple POS caches indicate that with appropriate smoothing the A language model is used in speech recognition perplexity, for example, on NN-words is decreased by systems and automatic translation systems to improve a factor of 2.5 with a cache-based conditional word the performance of such systems. A trigram language probability given the POS category instead of a static model [1], whose parameters are estimated from a probability model. large corpus of text (greater than a few million words), has been used successfully in both applications. The In [3], the dynamic model uses two bigram lan- trigram language model has a probability distribu- guage models, p,(w,.+l ] w,, D), where D=I for words tion for the next word conditioned on the previous w,+l that have occurred in the cache window and two words. This static distribution is obtained a.s an D=0 for words that have occurred in the cache win- average over many documents. Yet we know that sev- dow. For cache sizes from 128 to 4096, the reported I Now at Rutgers University, N J, work performed while vis- res,lts indicate an improvement in the average rank iting IBM of tile correct word predicted by the model by 7% to 293 Test static dynalnic ~c where p,(...) is the usual static trigram language model. Set Perplexity Perplexity We use the forward-backward algorithm to estimate A 9i 75 0.07 the interpolation parameter ),c [1]. This parameter B 53 49 0.12 varies between 0.07 and 0.28 depending on the par- C 262 202 0.07 ticular static trigram language model (we used tri- gram language models estimated from different size Table 1: Perplexity of static and dynamic language mod- corpora) and the cache size (varying from 200 to 1000 els. words.) We have evaluated this cache language model by Perplexity 262 217 computing the perplexity on three test sets: Table 2: Perplexity ~s ~ function of cache size on test set C. • Test sets A and B are each about 100k words of text that were excised from a corpus of docu- Static Unigram Trigram ments from an insurance company that was used Cache Cache for building the static trigram language model 262 230 202 for a 20,000-word vocabulary. • Test set C which consists of 7 documents (about Table 3: Perplexity of unigram and trigram caches. 4000 words) that were dictated in a field trial in the same insurance company on TANGORA 17% over the static model assuming one knows if the (the 20,000-word isolated word recognizer devel- next word is in the cache or not. oped at IBM.) In this paper, we will present a new cache lan- Table ] shows the perplexity of the static and dy- guage model and compare its performance to a tri- namic language models for the three test sets. The gram language model. In Section 2, we present our cache size was 1000 words and was updated word proposed dynamic component and some results com- synchronously. The static language model was esti- paring static and dynamic trigram language models mated from about 1.2 million words of insurance doc- using perplexity. In Section 3, we present our method uments. The dynamic language model yields from 8% for incorporating the dynamic language model in an to 23% reduction in perplexity, with the larger reduc- isolated 20,000 word speech recognizer and its effect tion occurring with the test sets with larger perplex- on recognition performance. ity. The interpolation weight Ac was estimated using set B when testing on sets A and C and set. A when CACHE LANGUAGE MODEL testing on set B. Table 2 shows the effect of cache size Using a window of the n most recent words, we can on perplexity where it appears that a larger cache is estimate a unigram frequency distribution f,(w,+l), more useful. These results were on test set C. On a bigram frequency distribution, fn(w,÷l I w,), and a test set C, the rate that the next word is in the cache trigram frequency distribution, f,(w,+l } w,,w,-1). ranges from 75% for a cache window of 200 words to The resulting 3 dynamic estimators are linearly smoothed 83% for a window of 1000. Table 3 compares a cache together to obtain a dynamic trigram model denoted with unigrams only with a full trigram cache (for the by pc,(w,+l I w,., w,-a). The dynamic trigram model trigram cache, the weights for the unigram, bigram, assigns a non-zero probability for the words that have and trigram frequencies were 0.25, 0.25, 0.5 respec- occurred in the window of the previous n words. Since tively and were selected by hand.) A second set of the next word may not be in the cache and since the weights (0.25,0.5,0.25) produced a perplexity of 190 cache contains very few trigrams, we interpolate lin- for the trigram cache. In all the above experiments, early the dynamic model with the the static trigram the cache was not flushed between documents. In the language model: next section, we compare the different models in an I = (1) isolated speech recognition experiment. [ w,,, w._l) + We have tried using a fancier interpolation scheme (1 - I w,,.,w,,.-1) where the reliance on the cache depends on the cur- 294 Text 0-100 100-200 200-300 300-400 400-500 500-800 Length % Reduction in Error 6.1% 5.3% 4.7% 10.5% 16.3% 23.8% Rate Table 4: Percentage reduction in error rate with trigr~m cache. rent word wn with the expectation that some words each document. will tend to be followed by bursty words whereas other words will tend to be followed by non-bursty words. In these experiments, the weights for interpolating We typically used about 50 buckets (or weighting pa- the dynamic unigram, bigram, and trigram hequen- rameters). However, we have found that the perplex- cies were 0.4, 0.5, and 0.1, respectively. The weight of ity on independent data to be no better than the single the cache probability, At, relative to the static trigram parameter interpolation. probability was 0.2. Small changes in this weight does not seem to affect recognition performance. The po- tential benefit of a cache depends on the amount of ISOLATED SPEECH RECOGNITION text that has been observed. Table 4 shows the per- We incorporated the cache language model into centage reduction in error rate as a function of the the TANGORA isolated speech recognition system. length of the observed text. We divided the docu- We evaluated two cache update strategies. In the first ments into 100-word bins and computed the error rate one, the cache is updated at the end of every utter- in each bin. For the static language model, the error ance, i.e., when the speaker turns off the microphone. rate should be constant except for statistical fluctua- An utterance may be a partial sentence or a complete tions, whereas one expects that the error rate of the sentence or several sentences depending on how the cache to decrease with longer documents.
Load more

Recommended publications
  • Improving Neural Language Models with A
    Published as a conference paper at ICLR 2017 IMPROVING NEURAL LANGUAGE MODELS WITH A CONTINUOUS CACHE Edouard Grave, Armand Joulin, Nicolas Usunier Facebook AI Research {egrave,ajoulin,usunier}@fb.com ABSTRACT We propose an extension to neural network language models to adapt their pre- diction to the recent history. Our model is a simplified version of memory aug- mented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented net- works. 1 INTRODUCTION Language models, which are probability distributions over sequences of words, have many appli- cations such as machine translation (Brown et al., 1993), speech recognition (Bahl et al., 1983) or dialogue agents (Stolcke et al., 2000). While traditional neural networks language models have ob- tained state-of-the-art performance in this domain (Jozefowicz et al., 2016; Mikolov et al., 2010), they lack the capacity to adapt to their recent history, limiting their application to dynamic environ- ments (Dodge et al., 2015). A recent approach to solve this problem is to augment these networks with an external memory (Graves et al., 2014; Grefenstette et al., 2015; Joulin & Mikolov, 2015; Sukhbaatar et al., 2015). These models can potentially use their external memory to store new information and adapt to a changing environment.
    [Show full text]
  • Formatting Instructions for NIPS -17
    System Combination for Machine Translation Using N-Gram Posterior Probabilities Yong Zhao Xiaodong He Georgia Institute of Technology Microsoft Research Atlanta, Georgia 30332 1 Microsoft Way, Redmond, WA 98052 [email protected] [email protected] Abstract This paper proposes using n-gram posterior probabilities, which are estimated over translation hypotheses from multiple machine translation (MT) systems, to improve the performance of the system combination. Two ways using n-gram posteriors in confusion network decoding are presented. The first way is based on n-gram posterior language model per source sentence, and the second, called n-gram segment voting, is to boost word posterior probabilities with n-gram occurrence frequencies. The two n-gram posterior methods are incorporated in the confusion network as individual features of a log-linear combination model. Experiments on the Chinese-to-English MT task show that both methods yield significant improvements on the translation performance, and an combination of these two features produces the best translation performance. 1 Introduction In the past several years, the system combination approach, which aims at finding a consensus from a set of alternative hypotheses, has been shown with substantial improvements in various machine translation (MT) tasks. An example of the combination technique was ROVER [1], which was proposed by Fiscus to combine multiple speech recognition outputs. The success of ROVER and other voting techniques give rise to a widely used combination scheme referred as confusion network decoding [2][3][4][5]. Given hypothesis outputs of multiple systems, a confusion network is built by aligning all these hypotheses. The resulting network comprises a sequence of correspondence sets, each of which contains the alternative words that are aligned with each other.
    [Show full text]
  • Knowledge-Powered Deep Learning for Word Embedding
    Knowledge-Powered Deep Learning for Word Embedding Jiang Bian, Bin Gao, and Tie-Yan Liu Microsoft Research {jibian,bingao,tyliu}@microsoft.com Abstract. The basis of applying deep learning to solve natural language process- ing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually con- tains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it. Fortunately, text itself already contains well- defined morphological and syntactic knowledge; moreover, the large amount of texts on the Web enable the extraction of plenty of semantic knowledge. There- fore, it makes sense to design novel deep learning algorithms and systems in order to leverage the above knowledge to compute more effective word embed- dings. In this paper, we conduct an empirical study on the capacity of leveraging morphological, syntactic, and semantic knowledge to achieve high-quality word embeddings. Our study explores these types of knowledge to define new basis for word representation, provide additional input information, and serve as auxiliary supervision in deep learning, respectively. Experiments on an analogical reason- ing task, a word similarity task, and a word completion task have all demonstrated that knowledge-powered deep learning can enhance the effectiveness of word em- bedding. 1 Introduction With rapid development of deep learning techniques in recent years, it has drawn in- creasing attention to train complex and deep models on large amounts of data, in order to solve a wide range of text mining and natural language processing (NLP) tasks [4, 1, 8, 13, 19, 20].
    [Show full text]
  • Recurrent Neural Network Based Language Model
    INTERSPEECH 2010 Recurrent neural network based language model Toma´sˇ Mikolov1;2, Martin Karafiat´ 1, Luka´sˇ Burget1, Jan “Honza” Cernockˇ y´1, Sanjeev Khudanpur2 1Speech@FIT, Brno University of Technology, Czech Republic 2 Department of Electrical and Computer Engineering, Johns Hopkins University, USA fimikolov,karafiat,burget,[email protected], [email protected] Abstract INPUT(t) OUTPUT(t) A new recurrent neural network based language model (RNN CONTEXT(t) LM) with applications to speech recognition is presented. Re- sults 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 empiri- cal evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high com- putational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition CONTEXT(t-1) 1. Introduction Figure 1: Simple recurrent neural network. Sequential data prediction is considered by many as a key prob- lem in machine learning and artificial intelligence (see for ex- ample [1]). The goal of statistical language modeling is to plex and often work well only for systems based on very limited predict the next word in textual data given context; thus we amounts of training data.
    [Show full text]
  • Unified Language Model Pre-Training for Natural
    Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1].
    [Show full text]
  • Hello, It's GPT-2
    Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems Paweł Budzianowski1;2;3 and Ivan Vulic´2;3 1Engineering Department, Cambridge University, UK 2Language Technology Lab, Cambridge University, UK 3PolyAI Limited, London, UK [email protected], [email protected] Abstract (Young et al., 2013). On the other hand, open- domain conversational bots (Li et al., 2017; Serban Data scarcity is a long-standing and crucial et al., 2017) can leverage large amounts of freely challenge that hinders quick development of available unannotated data (Ritter et al., 2010; task-oriented dialogue systems across multiple domains: task-oriented dialogue models are Henderson et al., 2019a). Large corpora allow expected to learn grammar, syntax, dialogue for training end-to-end neural models, which typ- reasoning, decision making, and language gen- ically rely on sequence-to-sequence architectures eration from absurdly small amounts of task- (Sutskever et al., 2014). Although highly data- specific data. In this paper, we demonstrate driven, such systems are prone to producing unre- that recent progress in language modeling pre- liable and meaningless responses, which impedes training and transfer learning shows promise their deployment in the actual conversational ap- to overcome this problem. We propose a task- oriented dialogue model that operates solely plications (Li et al., 2017). on text input: it effectively bypasses ex- Due to the unresolved issues with the end-to- plicit policy and language generation modules. end architectures, the focus has been extended to Building on top of the TransferTransfo frame- retrieval-based models.
    [Show full text]
  • N-Gram Language Modeling Tutorial Dustin Hillard and Sarah Petersen Lecture Notes Courtesy of Prof
    N-gram Language Modeling Tutorial Dustin Hillard and Sarah Petersen Lecture notes courtesy of Prof. Mari Ostendorf Outline: • Statistical Language Model (LM) Basics • n-gram models • Class LMs • Cache LMs • Mixtures • Empirical observations (Goodman CSL 2001) • Factored LMs Part I: Statistical Language Model (LM) Basics • What is a statistical LM and why are they interesting? • Evaluating LMs • History equivalence classes What is a statistical language model? A stochastic process model for word sequences. A mechanism for computing the probability of: p(w1, . , wT ) 1 Why are LMs interesting? • Important component of a speech recognition system – Helps discriminate between similar sounding words – Helps reduce search costs • In statistical machine translation, a language model characterizes the target language, captures fluency • For selecting alternatives in summarization, generation • Text classification (style, reading level, language, topic, . ) • Language models can be used for more than just words – letter sequences (language identification) – speech act sequence modeling – case and punctuation restoration 2 Evaluating LMs Evaluating LMs in the context of an application can be expensive, so LMs are usually evaluated on the own in terms of perplexity:: ˜ 1 PP = 2Hr where H˜ = − log p(w , . , w ) r T 2 1 T where {w1, . , wT } is held out test data that provides the empirical distribution q(·) in the cross-entropy formula H˜ = − X q(x) log p(x) x and p(·) is the LM estimated on a training set. Interpretations: • Entropy rate: lower entropy means that it is easier to predict the next symbol and hence easier to rule out alternatives when combined with other models small H˜r → small PP • Average branching factor: When a distribution is uniform for a vocabulary of size V , then entropy is log2 V , and perplexity is V .
    [Show full text]
  • A General Language Model for Information Retrieval Fei Song W
    A General Language Model for Information Retrieval Fei Song W. Bruce Croft Dept. of Computing and Info. Science Dept. of Computer Science University of Guelph University of Massachusetts Guelph, Ontario, Canada N1G 2W1 Amherst, Massachusetts 01003 [email protected] [email protected] ABSTRACT However, estimating the probabilities of word sequences can be expensive, since sentences can be arbitrarily long and the size of Statistical language modeling has been successfully used for a corpus needs to be very large. In practice, the statistical speech recognition, part-of-speech tagging, and syntactic parsing. language model is often approximated by N-gram models. Recently, it has also been applied to information retrieval. Unigram: P(w ) = P(w )P(w ) ¡ P(w ) According to this new paradigm, each document is viewed as a 1,n 1 2 n language sample, and a query as a generation process. The Bigram: P(w ) = P(w )P(w | w ) ¡ P(w | w ) retrieved documents are ranked based on the probabilities of 1,n 1 2 1 n n−1 producing a query from the corresponding language models of Trigram: P(w ) = P(w )P(w | w )P(w | w ) ¡ P(w | w ) these documents. In this paper, we will present a new language 1,n 1 2 1 3 1,2 n n−2,n−1 model for information retrieval, which is based on a range of data The unigram model makes a strong assumption that each word smoothing techniques, including the Good-Turing estimate, occurs independently, and consequently, the probability of a curve-fitting functions, and model combinations.
    [Show full text]
  • A Unified Multilingual Handwriting Recognition System Using
    1 A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units Wassim Swaileha,∗, Yann Soullarda, Thierry Paqueta aLITIS Laboratory - EA 4108 Normandie University - University of Rouen Rouen, France ABSTRACT We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity. c 2018 Elsevier Ltd. All rights reserved. 1. Introduction Offline handwriting recognition is a challenging task due to the high variability of data, as writing styles, the quality of the input image and the lack of contextual information. The recognition is commonly done in two steps. First, an optical character recognition (OCR) model is optimized for recognizing sequences of characters from input images of handwritten texts (Plotz¨ and Fink (2009); Singh (2013)).
    [Show full text]
  • Word Embeddings: History & Behind the Scene
    Word Embeddings: History & Behind the Scene Alfan F. Wicaksono Information Retrieval Lab. Faculty of Computer Science Universitas Indonesia References • Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003). A Neural Probabilistic Language Model. The Journal of Machine Learning Research, 3, 1137–1155. • Mikolov, T., Corrado, G., Chen, K., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. Proceedings of the International Conference on Learning Representations (ICLR 2013), 1–12 • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. NIPS, 1–9. • Morin, F., & Bengio, Y. (2005). Hierarchical Probabilistic Neural Network Language Model. Aistats, 5. References Good weblogs for high-level understanding: • http://sebastianruder.com/word-embeddings-1/ • Sebastian Ruder. On word embeddings - Part 2: Approximating the Softmax. http://sebastianruder.com/word- embeddings-softmax • https://www.tensorflow.org/tutorials/word2vec • https://www.gavagai.se/blog/2015/09/30/a-brief-history-of- word-embeddings/ Some slides were also borrowed from From Dan Jurafsky’s course slide: Word Meaning and Similarity. Stanford University. Terminology • The term “Word Embedding” came from deep learning community • For computational linguistic community, they prefer “Distributional Semantic Model” • Other terms: – Distributed Representation – Semantic Vector Space – Word Space https://www.gavagai.se/blog/2015/09/30/a-brief-history-of-word-embeddings/ Before we learn Word Embeddings... Semantic Similarity • Word Similarity – Near-synonyms – “boat” and “ship”, “car” and “bicycle” • Word Relatedness – Can be related any way – Similar: “boat” and “ship” – Topical Similarity: • “boat” and “water” • “car” and “gasoline” Why Word Similarity? • Document Classification • Document Clustering • Language Modeling • Information Retrieval • ..
    [Show full text]
  • Language Modelling for Speech Recognition
    Language Modelling for Speech Recognition • Introduction • n-gram language models • Probability estimation • Evaluation • Beyond n-grams 6.345 Automatic Speech Recognition Language Modelling 1 Language Modelling for Speech Recognition • Speech recognizers seek the word sequence Wˆ which is most likely to be produced from acoustic evidence A P(Wˆ |A)=m ax P(W |A) ∝ max P(A|W )P(W ) W W • Speech recognition involves acoustic processing, acoustic modelling, language modelling, and search • Language models (LMs) assign a probability estimate P(W ) to word sequences W = {w1,...,wn} subject to � P(W )=1 W • Language models help guide and constrain the search among alternative word hypotheses during recognition 6.345 Automatic Speech Recognition Language Modelling 2 Language Model Requirements � Constraint Coverage � NLP Understanding � 6.345 Automatic Speech Recognition Language Modelling 3 Finite-State Networks (FSN) show me all the flights give restaurants display • Language space defined by a word network or graph • Describable by a regular phrase structure grammar A =⇒ aB | a • Finite coverage can present difficulties for ASR • Graph arcs or rules can be augmented with probabilities 6.345 Automatic Speech Recognition Language Modelling 4 Context-Free Grammars (CFGs) VP NP V D N display the flights • Language space defined by context-free rewrite rules e.g., A =⇒ BC | a • More powerful representation than FSNs • Stochastic CFG rules have associated probabilities which can be learned automatically from a corpus • Finite coverage can present difficulties
    [Show full text]
  • A Study on Neural Network Language Modeling
    A Study on Neural Network Language Modeling Dengliang Shi [email protected] Shanghai, Shanghai, China Abstract 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 mod- els, 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 ex- plored 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 ma- trixes 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. Keywords: neural network language modeling, optimization techniques, limits, improve- ment scheme 1. Introduction Generally, a well-designed language model makes a critical difference in various natural language processing (NLP) tasks, like speech recognition (Hinton et al., 2012; Graves et al., 2013a), machine translation (Cho et al., 2014a; Wu et al., 2016), semantic extraction (Col- lobert and Weston, 2007, 2008) and etc.
    [Show full text]