[PDF] Top 20 Efficient Subsampling for Training Complex Language Models
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Efficient Subsampling for Training Complex Language Models
... A lot of previous research has focused on speeding up NNLM training. It usually aims at removing the computational dependency on V . Schwenk (2007) used a short list of frequent words such that a large number of ... See full document
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Efficient Minimal Perfect Hash Language Models
... for language models by storing less infor- ...the language model and so can be seen as comple- mentary to ...storing language models have been used: compact trie structures and hash ... See full document
8
Improved model identification for nonlinear systems using a random subsampling and multifold modelling (RSMM) approach
... the training data that are used for model identification including parameter estimation, and the test data that are used for model performance ...the training and test data using the ‘hold-out’ method, for ... See full document
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An efficient clustering algorithm for class based language models
... Table 1 lists the description lengths for training data from 1 to 32k and Table 2 shows the precision and cov- erage achieved by each method with this data. In these tables, we can see that our method works ... See full document
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A Legal Perspective on Training Models for Natural Language Processing
... This paper underlines the complexities in the relationship between concerning copyright and science in the context of ML/NLP. The legal analysis has been based on three specific scenarios which are all evolving around ... See full document
8
Strategies for Training Large Vocabulary Neural Language Models
... Language models assign a probability to a word given a context of preceding, and possibly sub- sequent, ...log-bilinear models (Mnih and Hinton, ... See full document
11
Sampling Informative Training Data for RNN Language Models
... guage models which use a set of binary classifiers to determine sequence likelihood, rather than cal- culating the probabilities jointly (Xu et ...these subsampling techniques are used to learn domain ... See full document
5
Training Hybrid Language Models by Marginalizing over Segmentations
... brid language modeling, that is using models which can predict both characters and larger units such as character ngrams or ...such models, multiple potential segmenta- tions usually exist for a ... See full document
6
Training Complex Models with Multi-Task Weak Supervision
... an efficient way for practitioners to supervise mod- ern machine learning models, including new multi-task vari- ants, for complex tasks by opportunistically using the di- verse weak supervision ... See full document
9
The Helsinki Neural Machine Translation System
... for training non-factored phrase-based SMT models using KenLM for language modeling (Heafield, 2011) and BLEU-based MERT for tun- ...an efficient implementation of fertility-based IBM word ... See full document
10
Training Continuous Space Language Models: Some Practical Issues
... space language models are becoming increas- ingly ...for language mod- els, and given rise to several new proposals (see, for instance, (Mnih and Hinton, 2007; Mnih and Hinton, 2008; Collobert and ... See full document
11
FLAIR: An Easy to Use Framework for State of the Art NLP
... facilitate training and distribution of state-of-the-art sequence labeling, text classi- fication and language ...model training and hyperparameter selection routines, as well as a data fetching ... See full document
6
Evaluation of language identification methods using 285 languages
... The language models are cre- ated by tokenizing the training texts for each lan- guage g into words and then padding each word with spaces, one before and four ... See full document
9
Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training
... EM training performs best, both in terms of performance and parameter ...per training sequence and per iteration are essentially the same within error ...two models, Viterbi training converges ... See full document
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Normalized Log Linear Interpolation of Backoff Language Models is Efficient
... Log-linear interpolation performs better on TED (72.58 perplexity versus 75.91 for offline lin- ear interpolation). However, it performs worse on news. In future work, we plan to investigate whether log-linear wins when ... See full document
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Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control
... the models, as well as training time in seconds, will be given in the ...Although training time is not regarded as an impor- tant aspect for offline classification, short training time has ... See full document
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Low Cost Enrichment of Spanish WordNet with Automatically Translated Glosses: Combining General and Specialized Models
... ments) suffice in this domain to obtain a signifi- cant improvement. Besides, all the methods used are language independent, assumed the availabil- ity of the required in-domain additional resources. In the future ... See full document
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Training Neural Network Language Models on Very Large Corpora
... word models (Katz, 1987), class models (Brown et ...structured language models (Chelba and Jelinek, 2000) or max- imum entropy language models (Rosenfeld, ...back-off ... See full document
8
Predicting and Eliciting Addressee’s Emotion in Online Dialogue
... While there have been many attempts to estimate the emotion of an addresser from her/his utterance, few studies have ex- plored how her/his utterance affects the emotion of the addressee. This has mo- tivated us to ... See full document
9
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task
... There is a growing literature on using web- acquired data for constructing various types of language resources, including monolingual and parallel corpora. As shown in, among others, Pecina et al. (2014) and ... See full document
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