... -gram language models are com-pressed into 10 GB, which is comparable to a lossyrepresentation (Talbot and Brants, 2008).2 N -gram Language Model We assume a back-off N-gram languagemodel ... GB Succinct 12.62 GB 10.57 GB Language Trie 42.65 GB 20.01 GB model Integer33.65 GB 18.98 GB Succinct 31.67 GB 18.37 GBQuantized Trie 24.73 GB 11.47 GB language Integer15.73 GB 10.44 GB model ... 23.59GBto 10.57GB by our succinct representation withblock compression. N-gram language models of42.65GB were compressed to 18.37GB. Finally,the 8-bit quantized N -gram language models arerepresented...
... that the n-gram languagemodel used by the MTsystem was much smaller than the 5-GRAM model, as they wereonly trained on the English sides of their parallel data.fect languagemodel might not ... UnitedStates were asked to compare translations using ourTREELET languagemodel as the language model feature to those using the 5-GRAM model. 12We had1000 such translation pairs rated by 4 separate ... TREELET model, we alsoshow results for the following baselines:5-GRAM A 5-gram interpolated Kneser-Ney model. PCFG-LA The Berkeley Parser in languagemodel mode.HEADLEX A head-lexicalized model...
... 5-gram/2-SLM+2-gram/4-SLM+5-gram/PLSA languagemodel improves both signif-icantly. Bear in mind that Charniak et al. (2003) in-tegrated Charniak’s languagemodel with the syntax-based translation model Yamada and ... Large language models in ma-chine translation. The 2007 Conference on EmpiricalMethods in Natural Language Processing (EMNLP),858-867.E. Charniak. 2001. Immediate-head parsing for language models. ... Dis-tributed language modeling for N-best list re-ranking.The 2006 Conference on Empirical Methods in Natu-ral Language Processing (EMNLP), 216-223.Y. Zhang, 2008. Structured language models for...
... incorporate large-scale n-gram language models in conjunction withincremental syntactic language models.The added decoding time cost of our syntactic language model is very high. By increasing ... sequencemodels as language models.Modern phrase-based translation using large scalen-gram language models generally performs wellin terms of lexical choice, but still often producesungrammatical ... parsing for language modeling, but do notuse this languagemodel in a translation system. Ourwork, in contrast to the above approaches, exploresthe use of incremental syntactic language models...
... f-score outperform state-of-the-art models re-709mary corpus for our model. The language model part of the noisy channel model already uses a bi-gram languagemodel based on Switchboard, but inthe ... productive. Indeed the best per-forming model is the model which has all extendedfeatures and all languagemodel features. The dif-ferences among the different language models whenextended features ... 19 other folds to construct a language model and then score the utterance in this fold with that language model. The largest widely-available corpus for language modelling is the Web 1T 5-gram...
... likelihoodof the test corpus under the train corpus language model (using basic Kneser-Ney) and the likelihoodof the test corpus under a jackknife language model from the test itself, which holds out ... of EnglishBigrams. Computer Speech & Language, 5(1):19–54.Joshua Goodman. 2001. A Bit of Progress in Language Modeling. Computer Speech & Language, 15(4):403–434.Bo-June (Paul) Hsu ... M-Gram Language Modeling. In Pro-ceedings of International Conference on Acoustics,Speech, and Signal Processing.Robert C. Moore and William Lewis. 2010. Intelligentselection of language model...
... accurate language model. For the current study, all language models wereestimated from a one million sentence (210M char-acter) sample of the NY Times portion of the EnglishGigaword corpus. Models ... before, theuser can take a break and then the system continueswith the next epoch.3 Language Modeling Language modeling is important for many text pro-cessing applications, e.g., speech recognition ... simple interfaces have yet to take full advan-tage of language models to ease or speed typing.In this demonstration, we will present a language- model enabled interface that is appropriate for themost...
... probabilities (PPs), which combines acoustic model and languagemodel scores after decoding.Based on the character PPs, we adapt the currentlexicon. The languagemodel is then re-trained ac-cording ... and Language Model TrainingIf we regard the word segmentation process as ahidden variable, then we can apply EM algorithm(Dempster et al., 1977) to train the underlying n-gram language model. ... words and also the buildingunits in the languagemodel (LM). Lexical wordsoffer local constraints to combine phonemes intoshort chunks while the languagemodel combinesphonemes into longer chunks...
... Goodman. 2001. A bit of progress in language modeling. Computer Speech and Language. R. Kneser and H. Ney. 1995. Improved backing-off form-gram language modeling. In International Confer-ence ... Bauman Peto. 1995. Ahierarchical Dirichlet language model. Natural Lan-guage Engineering, 1(3):1–19.Y.W. Teh. 2006. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceed-ings ... sure the probabilities are normalized. Theinterpolated models always incorporate the lower or-der distribution Pr(c|b) whereas the back-off modelsconsider it only when the n-gram abc has not beenobserved...
... methodfor estimating N-gram language models.Kneser-Ney smoothing, however, requiresnonstandard N-gram counts for the lower-order models used to smooth the highest-order model. For some applications, ... Kneser-Ney andthose methods.1 IntroductionStatistical language models are potentially usefulfor any language technology task that producesnatural -language text as a final (or intermediate)output. ... approach when language models based on ordinary counts are desired.ReferencesChen, Stanley F., and Joshua Goodman. 1998.An empirical study of smoothing techniques for language modeling. Technical...
... syntactic language models into aspeech recognizer. These methods have almost ex-clusively worked within the noisy channel paradigm,where the syntactic languagemodel has the taskof modeling ... Jelinek. 2000. Structured language modeling. Computer Speech and Language, 14(4):283–332.Ciprian Chelba. 2000. Exploiting Syntactic Structure for Nat-ural Language Modeling. Ph.D. thesis, The ... pass on these firstpass lattices, allowing for better silence modeling,and replaces the trigram languagemodel score witha 6-gram model. 1000-best lists were then extractedfrom these lattices....
... statistical language modeling, and language identification. A typical LID system is illustrated in Figure 1 (Zissman, 1996), where language dependent voice tokenizers (VT) and lan-guage models ... Tokenization at different resolutions 2.2 n-gram LanguageModel With the sequence of tokens, we are able to es-timate an n-gram languagemodel (LM) from the statistics. It is generally agreed ... the 1996 NIST Language Recognition Evaluation database. 1 Introduction Spoken language and written language are similar in many ways. Therefore, much of the research in spoken language identification,...
... Good-man, 1999). We used the SRI Language ModelingToolkit (Stolcke, 2002) for languagemodel training.Our first set of classifiers consists of one n-gram language model per class c in the set of ... Statistical Language ModelsStatistical LMs predict the probability that a partic-ular word sequence will occur. The most commonlyused statistical languagemodel is the n-gram model, which ... statistical language models.In this paper, we also use support vectormachines to combine features from tradi-tional reading level measures, statistical language models, and other language pro-cessing...
... efficient. 5 Experiments 5.1 Training Data for the Language Model We used the EDR Japanese Corpus Version 1.0 (EDR, 1991) to train the language model. It is a corpus of approximately 5.1 million ... dictionary. 3.2 Word Model for Unknown Words We defined a statistical word model to assign a rea- sonable word probability to an arbitrary substring in the input sentence. The word model is formally ... correction candidate is selected by the word segmentation algo- rithm using the OCR model and the language model. For simplicity, we will present the method as if it were for an isolated word...
... performance.3 Language modellingTo generate the different trigram language modelswe used the SRI language modelling toolkit (Stol-cke, 2002) with Good-Turing discounting.The first model was generated ... decades of statistical language modeling: Where do we go from here? In Proceed-ings of IEEE:88(8).Rosenfeld R. 2000. Incorporating Linguistic Structureinto Statistical Language Models. In PhilosophicalTransactions ... Statistical Language Modeling Using the CMU-CambridgeToolkit. In Proceedings of Eurospeech.Fosler-Lussier E. and Kuo H K. J. 2001. Using Se-mantic Class Information for Rapid Development of Language Models...