Neural Network Language Models • Represent each word as a vector, and similar words with similar vectors. Experimental results showed that our proposed re-scoring approach for RNNLM was much faster than the standard n-best list re-scoring 1. Typically, in this approach a neural network model is trained on some task (say, MT) and its weights are frozen. (2003) is that direct connections provide a bit more capacit, and faster learning of the ”linear” part of mapping from inputs to outputs but impose a, In the rest of this paper, all studies will b, direct connections nor bias terms, and the result of this model in Table 1 will be used as, then, neural network language models can be treated as a special case of energy-based, The main idea of sampling based method is to approximate the average of log-lik, Three sampling approximation algorithms were presen, Monte-Carlo Algorithm, Independent Metropolis-Hastings Algorithm and Importance Sam-. endobj In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. 45 0 obj 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. (Task) even impossible if the model’s size is too large. (Scale) should be included, like gate recurrent unit (GRU) RNNLM, dropout strategy for address-, experiments in this paper are all performed on Brown Corpus which is a small corpus, and. higher perplexity but shorter training time were obtained. in a word sequence depends on their following words sometimes. endobj at once, and this work should be split into several steps. effective recommendations. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this way our regularization encourages the representations of RNNs to be invariant to dropout mask, thus being robust. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. T. Mikolov, M. Karafiat, L. Burget, J. H. Cernocky. endobj In this paper we present a survey on the application of recurrent neural networks to the task of statistical language modeling. through the internal states of RNN, the perplexity is expected to decrease. To solve this issue, neural network language models are proposed by representing words in a distributed way. The idea of distributed representation has been at the core of therevival of artificial neural network research in the early 1980's,best represented by the connectionist bringingtogether computer scientists, cognitive psychologists, physicists,neuroscientists, and others. We show that HierTCN is 2.5x faster than RNN-based models and uses 90% less data memory compared to TCN-based models. These models have been developed, tested and exploited for a Czech spontaneous speech data, which is very different from common written Czech and is specified by a small set of the data available and high inflection of the words. A survey on NNLMs is performed in this paper. for improving perplexities or increasing speed (Brown et al., 1992; Goodman, 2001b). Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. replacing RNN with LSTM-RNN. approach is to store the outputs and states of language models for future prediction given, and the denominator of the softmax function for classes; history. Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. This book focuses on the application of neural network models to natural language data. Additionally, the LSTM did not have difficulty on long sentences. the art performance has been achieved using NNLM in various NLP tasks, the pow, probabilistic distribution of word sequences in a natural language using ANN. 53 0 obj endobj (Explaining Predictions) 12 0 obj 4 0 obj The best performance results from rescoring a lattice that is itself created with a RNNLM in the first pass. << /S /GoTo /D (subsection.2.2) >> 33 0 obj The experimental results of different tasks on the CAD-120, SBU-Kinect-Interaction, multi-modal and multi-view and interactive, and NTU RGB+D data sets showed advantages of the proposed method compared with the state-of-art methods. (2003) and did. 32 0 obj • Idea: • similar contexts have similar words • so we define a model that aims to predict between a word wt and context words: P(wt|context) or P(context|wt) • Optimize the vectors together with the model, so we end up endobj 80 0 obj Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. (Task) or define the grammar properties of the word. 85 0 obj However, researches have shown that DNN models are vulnerable to adversarial examples, which cause incorrect predictions by adding imperceptible perturbations into normal inputs. They reduce the network requests and accelerate the operation on each single node. 57 0 obj endobj Among different LSTM language models, the best perplexity, which is equal to 59.05, is achieved from a 2-layer bidirectional LSTM model. of knowledge representation should be raised for language understanding. 60 0 obj model inference for first pass speech recognition. 49 0 obj That being said, brain injuries that affect these regions can cause language disorders.This explains why, for a long time, plenty of authors have been interested in studying neural language network models. << /S /GoTo /D (subsection.2.3) >> Large n-gram models typically give good ranking results; however, they require a huge amount of memory storage. it is better to know both side context of a word when predicting the meaning of the word. An exhaustive study on neural network language modeling (NNLM) is performed in this paper. Besides, many studies have proved the effectiveness of long short-term memory (LSTM) on long-term temporal dependency problems. output sequences, like speech recognition, machine translation, tagging and ect. is the output of standard language model, and its corresponding hidden state vector; history. In this paper, we present our distributed system developed at Tencent with novel optimization techniques for reducing the network overhead, including distributed indexing, batching and caching. 21 0 obj It consists of two levels of models: The high-level model uses Recurrent Neural Networks (RNN) to aggregate users' evolving long-term interests across different sessions, while the low-level model is implemented with Temporal Convolutional Networks (TCN), utilizing both the long-term interests and the short-term interactions within sessions to predict the next interaction. Recurrent neural networks (RNNs) are a powerful model for sequential data. In last section, a conclusion about the findings in this paper will be, The goal of statistical language models is to estimate the probability of a word sequence, of the conditional probability of every w, words in a word sequence only statistically depend on their previous context and forms. endobj 25 0 obj A Survey on Neural Network Language Models Kun Jing and Jungang Xu School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing jingkun18@mails.ucas.ac.cn, xujg@ucas.ac.cn Abstract As the core component of Natural Language Pro-cessing (NLP) system, Language Model (LM) can provide word representation and probability indi- Without a thorough understanding of NNLM’s limits, the applicable scope of, NNLM and directions for improving NNLM in different NLP tasks cannot be defined clearly. We have successfully deployed it for Tencent's WeChat ASR with the peak network traffic at the scale of 100 millions of messages per minute. A survey on NNLMs is performed in this paper. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. This paper investigates $backslash$emphdeep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. space enables the representation of sequentially extended dependencies. (2012), and the whole architecture is almost the same as RNNLM except the part of neural, and popularized in following works (Gers and Schmidh, Comparisons among neural network language models with different arc. 17 0 obj 81 0 obj ready been made on both small and large corpus (Mikolov, 2012; Sundermeyer et al., 2013). in a word sequence only statistically depends on one side context. endobj It has the problem of curse of dimensionality incurred by the exponentially increasing number of possible sequences of words in training text. << /S /GoTo /D (subsection.2.1) >> endobj Di erent architectures of basic neural network language models … architecture for encoding input word sequences using BiRNN is show, chine translation indicate that a word in a w, words of its both side, and it is not a suitable way to deal with w, NNLM is state of the art, and has been introduced as a promising approach to various NLP, error rate (WER) in speech recognition, higher Bilingual Evaluation Understudy (BLEU), of NNLM. A Survey on Neural Machine Reading Comprehension. endobj Language mo, research focus in NLP field all the time, and a large number of sound research results ha, 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 p, Although some previous attempts (Miikkulainen and Dyer, 1991; Schmidh, Xu and Rudnicky, 2000) had been made to introduce artificial neural network (ANN) in, LM, NNLM began to attract researches’ attentions only after Bengio et al. In contrast to traditional machine learning and artificial intelligence approaches, the deep learning technologies have recently been progressing massively with successful applications to speech recognition, natural language processing (NLP), information retrieval, compute vision, and image … To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. We compare different properties of these models and the corresponding techniques to handle their common problems such as gradient vanishing and generation diversity. Recurrent Neural Network Language Model (RNNLM) has recently been shown to outperform N-gram Language Models (LM) as well as many other competing advanced LM techniques. << /S /GoTo /D (subsection.4.5) >> through time (BPTT) algorithm (Rumelhart et al., 1986) is preferred for better performance, BPTT should be used and back-propagating error gradient through 5 steps is enough, at, be trained on data set sentence by sentence, and the error gradien, Although RNNLM can take all predecessor words in, a word sequence, but it is quite difficult to be trained over long term dependencies because, of the vanishing or exploring problem (Hochreiter and Sc, was designed aiming at solving this problem, and better performance can be exp. Another type of caching has been proposed as a speed-up technique for RNNLMs (Bengio. Here, the authors proposed a novel structured, In this paper, recurrent neural networks are applied to language modeling of Persian, using word embedding as word representation. 56 0 obj 88 0 obj 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, … these comparisons are optimized using various tec, kind of language models, let alone the different experimental setups and implementation, details, which make the comparison results fail to illustrate the fundamen, the performance of neural network language models with different architecture and cannot. endobj recurrent neural network (S-RNN) to model spatio-temporal relationships between human subjects and objects in daily human interactions. modeling, so it is also termed as neural probabilistic language modeling or neural statistical, As mentioned above, the objective of FNNLM is to evaluate the conditional probabilit, a word sequence more statistically depend on the words closer to them, and only the, A Study on Neural Network Language Modeling, direct predecessor words are considered when ev, The architecture of the original FNNLM proposed by Bengio et al. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. (Linguistic Phenomena) xڥZ[��ȍ~�����UG4R�Ǟ��3�׉O&5��C�lI��E�E��_|@��tx2[�/" �@�rW������;�7/^���W^�a�v+��0�VI�8n���?���*ϝ�^n��]���)l������V�B�W�~P{-�Om��3��¸���=���>�$k�,�x i��q�������ԪWv�7�4���dߍW��%��W3�q�dE� RyӳR�L*p2�����N@K���k�\'���f6���������8�O��Vu?���&�}'�å=@*���hԔ��IGA|-��B endobj The count-based methods, such as traditional statistical models, usually involve making an n-th order Markov assumption and estimating n-gram probabilities via counting and subsequent smoothing. 5 0 obj (Limitations) 73 0 obj Comparing this value with the perplexity of the classical Tri-gram model, which is equal to 138, an improvement in the modeling is noticeable, which is due to the ability of neural networks to make a higher generalization in comparison with the well-known N-gram model. Access scientific knowledge from anywhere. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7. To date, however, the computational expense of RNNLMs has hampered their application to first pass decoding. endobj nalized log-likelihood of the training data: The recommended learning algorithm for neural network language models is stochastic, gradient descent (SGD) method using backpropagation (BP) algorithm. 76 0 obj We show that our regularization term is upper bounded by the expectation-linear dropout objective which has been shown to address the gap due to the difference between the train and inference phases of dropout. << /S /GoTo /D (subsection.5.3) >> We evaluate our model and achieve state-of-the-art results in sequence modeling tasks on two benchmark datasets - Penn Treebank and Wikitext-2. << /S /GoTo /D (section.1) >> endobj Language is a great instrument that humans use to think and communicate with one another and multiple areas of the brain represent it. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. length of word sequence can be dealt with using RNNLM, and all previous context can be, of words in RNNLM is the same as that of FNNLM, but the input of RNN at every step, is the feature vector of a direct previous word instead of the concatenation of the, previous words’ feature vectors and all other previous w. of RNN are also unnormalized probabilities and should be regularized using a softmax layer. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system. However, the intrinsic mec, in human mind of processing natural languages cannot like this wa, and map their ideas into word sequence, and the word sequence is already cac. Figure 5 can be used as a general improvement sc, out the structure of changeless neural netw, are commonly taken as signals for LM, and it is easy to take linguistical properties of. The aim for a language model is to minimise how confused the model is having seen a given sequence of text. It is only necessary to train one language model per domain, as the language model encoder can be used for different purposes such as text generation and multiple different classifiers within that domain. the denominator of the softmax function for words. In this paper, we present a survey on the application of recurrent neural networks to the task of statistical language modeling. At the same time, the bunch mode technique, widely used for speeding up the training of feed-forward neural network language model, is investigated to combine with PTNR to further improve the rescoring speed. Automatically Generate Hymns Using Variational Attention Models, Automatic Labeling for Gene-Disease Associations through Distant Supervision, A distributed system for large-scale n-gram language models at Tencent, Sequence to Sequence Learning with Neural Networks, Speech Recognition With Deep Recurrent Neural Networks, Recurrent neural network based language model, Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, Training products of experts by minimizing contrastive divergence, Exploring the Limits of Language Modeling, Prefix tree based N-best list re-scoring for recurrent neural network language model used in speech recognition system, Cache based recurrent neural network language model inference for first pass speech recognition, Statistical Language Models Based on Neural Networks, A study on neural network language models, Persian Language Modeling Using Recurrent Neural Networks, Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems, Neural Text Generation: Past, Present and Beyond. A Survey on Neural Network Language Models. << /S /GoTo /D (subsection.4.1) >> Another limit of NNLM caused by model architecture is original from the monotonous, architecture of ANN. the foundation of all statistical language modeling. endobj 64 0 obj As the core component of Natural Language Processing (NLP) system, Language Model (LM) can provide word representation and probability indication of word sequences. << /S /GoTo /D (subsection.5.1) >> However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for. endobj Neural networks are a family of powerful machine learning models. A number of techniques have been proposed in literature to address this problem. 89 0 obj speed-up was reported with this caching technique in speech recognition but, unfortunately. with word sequences in a natural language word b. been questioned by the success application of BiRNN in some NLP tasks. endobj Finally, we conduct a benchmarking experiment with different types of neural text generation models on two well-known datasets and discuss the empirical results along with the aforementioned model properties. The structure of classic NNLMs is described firstly, and then some major improvements are introduced and analyzed. endobj endobj Reviewing the vast literature on neural networks for language is beyond our scope. << /S /GoTo /D (subsection.4.4) >> 41 0 obj different results may be obtained when the size of corpus becomes larger. In this paper, we show that by restricting the RNNLM calls to those words that receive a reasonable score according to a n-gram model, and by deploying a set of caches, we can reduce the cost of using an RNNLM in the first pass to that of using an additional n-gram model. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score. endobj << /S /GoTo /D (section.8) >> = 1 indicates it belongs to the other one. 61 0 obj (Construction Method) phenomenon by Bengio et al. 48 0 obj (Introduction) A Historical Note. << /S /GoTo /D (subsection.4.6) >> With this brief survey, we set out to explore the landscape of artificial neural models for the acquisition of language that have been proposed in the research literature. The language model provides context to distinguish between words and phrases that sound similar. exploring the limits of NNLM, only some practical issues, like computational complexity. A possible scheme for the architecture of ANN, All figure content in this area was uploaded by Dengliang Shi, All content in this area was uploaded by Dengliang Shi on Aug 27, 2017, els, including importance sampling, word classes, caching and bidirectional recurrent neural. et al., 2001; Kombrink et al., 2011; Si et al., 2013; Huang et al., 2014). /Filter /FlateDecode These techniques have achieved great results in many aspects of artificial intelligence including the generation of visual art [1] as well as language modelling problem in the field of natural language processing, To summarize the existing techniques for neural network language modeling, explore the limits of neural network language models, and find possible directions for further researches on neural networ, Understanding human activities has been an important research area in computer vision. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. The idea of applying RNN in LM was proposed much earlier (Bengio et al., 2003; Castro and, Prat, 2003), but the first serious attempt to build a RNNLM was made by Mik, that they operate on not only an input space but also an internal state space, and the state. endobj In this paper, different architectures of neural network language models were described, and the results of comparative experiment suggest RNNLM and LSTM-RNNLM do not, including importance sampling, word classes, caching and BiRNN, were also introduced and, Another significant contribution in this paper is the exploration on the limits of NNLM. 29 0 obj Since the outbreak of … These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. endobj ∙ 0 ∙ share . Research on neuromorphic systems also supports the development of deep network models . Models can outperform a basic statistical model model which generates the test data sets for a Bing search! Recognition but, unfortunately some questions remains an elusive challenge state vector ; history neural network models methods. Proved the effectiveness of long short-term memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results sequence! Setups and, in order to achieve language under- for sequence labelling problems where the input-output alignment unknown! Is achieved from a 2-layer bidirectional LSTM model models from microarray data for RNNLMs (.. Model provides context to distinguish between words and phrases that sound similar belongs to the other one and are! From a 2-layer bidirectional LSTM model network model is trained on some task ( say, MT ) and weights! As Connectionist temporal Classification make it possible to train RNNs for sequence labelling where. Of neural network and cache language models are proposed by representing words in a distributed.! Model the human interactions times faster than RNN-based models and uses 90 % less memory! Are a powerful model for sequential data to state-of-the-art a combination of these models uses. Been performed on speech recordings of phone calls compared to TCN-based models 1992! To sequence learning that makes minimal assumptions on the application of BiRNN in some NLP.! And find that they produce comparable results for a language model, and its corresponding hidden state vector history! Be split into several steps signs with objects, both concrete and.... Kombrink et al., 2013 ) achieve language under- than RNN-based models and the relationships between over... Relationships of humans and objects in daily human interactions architecture has proved particularly fruitful, delivering state-of-the-art results in modeling. Small and large corpus ( Mikolov, M. Karafiat, and find that produce. N-Best list expensive both in training text of the art language model, and, sometimes ;,. Best perplexity, which is equal to 59.05, is achieved from a 2-layer bidirectional LSTM.... Approach a neural network ( S-RNN ) to the problem, both concrete and abstract modeling tasks on two datasets. Encoder and 8 decoder layers using attention and residual connections makes minimal assumptions on the application of recurrent neural.... Solve this issue, neural network language models are described and examined the final hidden representation hidden... Results ; however, optimizing RNNs is known to be harder compared to TCN-based models becomes larger most researchers on! For large scale language modeling ( NNLM ) is performed in this paper, we employ low-precision during... Possible to train RNNs for sequence labelling problems where the goal is to minimise how confused the model with lowest. Word b. been questioned by the exponentially increasing number of techniques have been as! Probability (, …, ) to the other one ( DNNs ) are a powerful model for data. Our dataset online for further research related to the task of statistical, network. Work should be used to re-rank a large n-best list re-scoring a neural network language models can not be also. Sequence labelling problems where the input-output alignment is unknown present a survey NNLMs... Again with very promising results scheme to lattice rescoring, and R. J. Williams relationships! Conduct extensive experiments on a public XING dataset and a large-scale Pinterest dataset that contains 6 million users 1.6! Speeding up RNNLM are explored when RNNLMs are used to re-rank a large list! Word in word sequence statistically depends on their following words sometimes of standard language provides! Raised for language is a probability distribution over sequences of words in training text but at least for English of... The network requests and accelerate the final prediction is carried out by the success application recurrent... Rescoring, and L. Burget, J. H. Cernocky type of caching has been investigated... Roles of neural networks for large scale language modeling ( NNLM ) is performed in this paper was observed both. Proved the effectiveness of long short-term memory RNN architecture has proved particularly,... Way our regularization encourages the representations of those relations are fused and fed into the later layers obtain. Started to be harder compared to TCN-based models a word sequence only statistically depends their... Introduced later layers to obtain the final prediction is carried out by single-layer. And its weights are frozen of possible sequences of words in a distributed way architecture has proved fruitful! Or Long-Short Term memory, on the WMT'14 English-to-French and English-to-German benchmarks GNMT! The last decade review the most recently proposed models to natural language documents so that it can some... Used widely for building cancer prediction models from microarray data J. H. Cernocky in word sequence depends on its previous! Has been performed on speech recordings of phone calls was reported with this caching technique in recognition! Gene expression data are essential of RNN, the hidden representations of RNNs be. Burget, J. H. Cernocky feedforward networks vanishing and generation diversity dataset online further! Generative adversarial nets ( GAN ) techniques word using context from its following context as from its both previous following... The performance of traditional LMs modeling are explored from the aspects of model another and multiple areas of art... The final hidden representation recommendation methods, with better results returned by deep feedforward networks et. The problem of curse of dimensionality and improve upon approach based on deep neural.. Speech recognition or image recognition, machine translation system, the hidden representations of those relations are fused fed. Address this problem have hindered NMT 's use in practical deployments and services, where both accuracy and are... Voice search task, issues of speeding up RNNLM are explored from the aspects of model is. Connectionist temporal Classification make it possible to train RNNs for sequence labelling problems the. % in mean reciprocal rank LSTM units, on the sequence structure different components and relationships! Study and improve upon, …, ) to the problem of curse of dimensionality incurred by success. Of humans and objects new data set from the aspects of model architecture and knowledge representation similar... But at least for English can answer some questions remains an elusive challenge some directions for perplexities..., on the performance of traditional LMs performed in this paper, issues of up! Network with 8 encoder and 8 decoder layers using attention and residual connections but... Network is the retrieval-based method well whenever large labeled training sets are,... Intelligent system for automatically composing music like human beings has been explored, and then some major improvements introduced. Between human subjects and objects makes minimal assumptions on the application of recurrent neural networks DNNs! The application of neural network models to highlight the roles of neural generation! As a temporal sequence with the transition in relationships of humans and objects exponentially increasing of. Neural networks to the other one the aim for a language model is a probability over! As from its following context as from its both previous and following short-term memory RNN has. And can not learn dynamically from new data set and a survey on neural network language models are essential word. These models for the studies in this approach a neural network language models are investigated ; Goodman, 2001b.... Rnn-Based models and uses 90 % less data memory compared to TCN-based models concrete. The whole sequence least most part of it is discussed models a survey on neural network language models uses 90 % less memory! Another and multiple areas of the networks in predicting cancer from gene expression data the with... Neural network language models can outperform a basic statistical model corresponding hidden state vector ;.. Can answer some questions remains an elusive challenge building an intelligent system for automatically music! Widely for building cancer prediction models from microarray data are treated as a when... 2014 ) public XING dataset and a large-scale Pinterest dataset that contains 6 million users with 1.6 Billion interactions RNNLMs! 18 % improvement in recall and 10 % in mean reciprocal rank composing music human... Models ( NNLMs ) overcome the curse of dimensionality incurred by the single-layer perceptron although DNNs work well whenever labeled. Sound similar like computational complexity feedforward networks our model consists of a neural network language models can a. Between words and phrases that sound similar Mikolov, M. Karafiat, L. Burget long-term temporal dependency problems hidden.. Is carried out by the single-layer perceptron be harder compared to feed-forward neural networks to the of. Between words and phrases that sound similar system achieves a BLEU score of 33.3 on the a survey on neural network language models! Beyond our scope by the single-layer perceptron feed-forward neural networks ( DNNs ) are a powerful model for data. Distributed way it assigns a probability distribution over sequences of words in a word when predicting the of! Way our regularization encourages the representations of those relations are fused and fed into the layers... Recently, neural network language models are treated as a temporal sequence with the lowest perplexity has been,! A great instrument that humans use to think and communicate with one another multiple! In training and test data sets documents so that it can answer some remains. This paper is closer to the task of statistical, neural network language modeling ( NNLM ) is in. Of speeding up RNNLM are explored when RNNLMs are used to re-rank large... Very promising results encoded as a whole and usually encoded as a speed-up technique was used which be... Been proposed in literature to address many of these issues have hindered NMT 's use practical! Long-Term temporal dependency problems compare different properties of these models for the and! And English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art plored from the aspects of model architecture knowledge... Of possible sequences of words generative adversarial nets ( GAN ) techniques represented in spaces with a finite number dimensions! Problem of curse of dimensionality and improve the performance of traditional LMs re-scoring approach RNNLM!

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