... Vogel.2006. Distributed Language Modeling for N-best ListRe-ranking. In Proc. of EMNLP 2006, pages 216-223.Bing Zhao, Matthias Eck and Stephan Vogel. 2004. Language ModelAdaptationfor Statistical ... translation mod-el adaptation and languagemodel adaptation. Herewe focus on how to adapt a translation model, whichis trained from the large-scale out-of-domain bilin-gual corpus, for domain-specific ... enforces one-to-one topic corre-spondence and enables latent topic distributions tobe efficiently transferred across languages, to cross-lingual language modeling and translation lexicon adaptation. ...
... USA. Association for Computational Linguistics.Bing Zhao, Matthias Eck, and Stephan Vogel. 2004. Language modeladaptationfor statistical machinetranslation with structured query models. In Proceed-ings ... Association for Computational Linguistics:shortpapers, pages 445–449,Portland, Oregon, June 19-24, 2011.c2011 Association for Computational LinguisticsOn-line LanguageModel Biasing for Statistical ... translation: parameterestimation. Computational Linguistics, 19:263–311.Woosung Kim. 2005. LanguageModelAdaptation for Automatic Speech Recognition and Statistical MachineTranslation. Ph.D. thesis,...
... was 0.001 for almost all training runs. The language model weight µ of the reduced model was about 60%smaller than the respective value for the full model, which confirms that the full model provides ... level of < 0.1% for both tests. The improve-ment of the full model compared to the reduced model is weakly significant on a level of 2.6% for the MAPSSWE test. For both models, the optimal ... |(1)The languagemodel weight λ and the word inser-tion penalty ip lead to a better performance in prac-tice, but they have no theoretical justification. Ourgrammar-based languagemodel is...
... 115–119,Jeju, Republic of Korea, 8-14 July 2012.c2012 Association for Computational LinguisticsTopic Models for Dynamic Translation Model Adaptation Vladimir EidelmanComputer Scienceand UMIACSUniversity ... for the source itcame from, many word pairs will be unobserved for a given table. This sparsity requires smoothing. Sec-ond, we may not know the (sub)corpora our training1 Language modeladaptation ... topic-specific contexts, wheretopics are induced in an unsupervised wayusing topic models; this can be thought ofas inducing subcorpora foradaptation with-out any human annotation. We use these...
... Syntax-based language models for statistical machine transla-tion. MT Summit IX., Intl. Assoc. for Machine Trans-lation.C. Chelba and F. Jelinek. 1998. Exploiting syntacticstructure forlanguage modeling. ... 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 statisti-cal ... n-gram/m-SLM/PLSA language model. The composite n-gram/m-SLM/PLSA lan-guage model can be formulated as a directedMRF model (Wang et al., 2006) with lo-cal normalization constraints for the param-eters...
... (CCN) Adaptation CorpusLexicon Adaptation for Improved Character AccuracyAdd/Delete wordsLexicon(Lexi) Language Model (LMi)y(LAICA)Word SegmentationLMTraining (Lexi) Model ... can beamended by involving the discriminative language modeladaptation in the iteration, which results ina unified languagemodel and lexicon adaptation framework. This can be our future work. ... beginning we are given an adaptation spokencorpus and manual transcriptions. Based on a base-line lexicon (Lex0) and a languagemodel (LM0)we perform ASR on the adaptation corpus and con-struct...
... June 2005.c2005 Association for Computational LinguisticsA Phonotactic LanguageModelfor Spoken Language Identification Haizhou Li and Bin Ma Institute for Infocomm Research Singapore ... Chen of Institute for Info-comm Research for insightful discussions. References Jerome R. Bellegarda. 2000. Exploiting latent semantic information in statistical language modeling, In Proc. ... in the formalism mentioned above: tokenization, statistical language modeling, and language identification. A typical LID system is illustrated in Figure 1 (Zissman, 1996), where language...
... Markov language model, and a simple set of unification grammar rules for the Chinese language, although the present model is in fact language independent. The system is written in C language ... signal preprocessor is included to form a complete speech recognition system. The language processor consists of a languagemodel and a parser. The languagemodel properly integrates the unification ... summarized. The Laneua~e Model The goal of the languagemodel is to participate in the selection of candidate constituents for a sentence to be identified. The proposed language model is composed...
... use216.1 The language model: probabilities andsmoothing For our language model, we need a list of Frenchlemmas with their frequencies of occurrence. Get-ting robust estimates for a large number ... astatistical languagemodel and a measure of tensedifficulty.4.1 The language model The lexical difficulty of a text is quite an elaboratephenomenon to parameterise. The logistic regres-sion models ... also ensures thatthe language resembles present-day spokenFrench.• The target population for our formula isyoung people and adults. Therefore, onlytextbooks intended for this public were...
... 2006. Unsupervised language modeladaptation using latent semantic marginals. InProc. of Interspeech.Y. C. Tam and T.Schultz. 2007. Correlated latent seman-tic modelforunsupervisedlanguagemodel ... propose a bilingualLSA model (bLSA) for crosslingual LM adaptation that can be applied before translation. The bLSA model consists of two LSA models: one for eachside of the language trained on ... marginal adaptation (Kneser et al., 1997)In this paper, we propose a framework to per-form LM adaptation across languages, enabling the adaptation of a LM from one language based on theadaptation...