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A Graph-based Cross-lingual Projection Approach for Spoken LanguageUnderstanding Portability to a New LanguageSeokhwan Kim...
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A Graph-based Cross-lingual Projection Approach for Spoken Language Understanding Portability to a New Language

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A Graph-based Cross-lingual Projection Approach for Spoken Language Understanding Portability to a New Language

  1. 1. A Graph-based Cross-lingual Projection Approach for Spoken LanguageUnderstanding Portability to a New LanguageSeokhwan KimHuman Language Technology Department, Institute for Infocomm Research, SingaporeIntroductionStatistical approaches to SLU require a sufficient number oftraining examples to obtain good resultsCross-lingual SLU using SMT technologies can improve theportability of SLU to a new languagePrevious work on cross-lingual SLU have focused on filteringout or correcting the noisy translations as post-processingWe propose a graph-based projection approach to improvethe robustness to the translation errors in cross-lingual SLUCross-lingual SLU Using SMTTrainOnTargetDatasetin LsSMTfrom Ls to LtTranslatedDatasetin LtSLUin LtUser Inputin LtTestTrainingAnnotations for a given word sequence x = {x1, · · · , xn}NEan NE tag sequence y = {y1, · · · , yn}DAa class variable zExamplexsyszsxtytztShow me flights to New York on Nov 18thî &â 11* 18Ò Êë ß ÃK š JBnto.city-bto.city-imonth-bday-bo ooooto.city-bmonth-bday-bo o o oo oshow_flightshow_flightDirect ProjectionThe simplest way of projectionIt propagates the annotations only with word alignments themselvesIt considers only the translation for each single utteranceIt is performed by a single pass processThe results of direct projection can be unreliable because oferroneous translations and word alignmentsGraph-based ProjectionGraph Construction for NENodesAll trigrams in the datasetEdgesMonolingual: w(vi, vj) = simcosine(f(vi), f(vj)) =f(vi)·f(vj)|f(vi)||f(vj)|Bilingual: w(vks , vlt ) =count(vks ,vlt )vmtcount(vks ,vmt )Initial valuesBased on the manual annotations of NE in LsvtvtvtvtvsvsvsvsGraph Construction for DANodesUtterance nodes U = {u1, · · · , um}Trigram nodes VEdgesThe edge between ui and vj has a binary weight value indicating whether vj in uiInitial valuesBased on the manual annotations of DA in LsututvtvtvtvtvsvsvsvsususLabel PropagationA graph-based semi-supervised learning algorithmIt induces labels for all of the unlabeled nodes on the graphExperimental SettingsData3,351 pairs of bi-utterances in English and KoreanManually annotated with 30 DA classes and 30 NE classesToolkitsMoses and SRILM for SMTJunto toolkit for Graph-based projectionMaximum Entropy for DA identificationConditional Random Fields for NE recognitionMeasures5-fold cross validation to the manual annotations on LtPrecision/recall/F-measure for NE recognitionAccuracy for DA identification.Experimental resultsNEKorean→English English→KoreanP R F P R FSupervised 97.6 95.4 96.4 97.1 96.9 97.0TestOnSource 45.2 16.4 24.0 63.8 19.9 30.3Direct 43.1 11.9 18.7 50.9 14.8 23.0Graph-based 50.7 39.8 44.6 67.2 43.4 52.7DAAccuracy (%)Korean→English English→KoreanSupervised 87.7 83.3TestOnSource 58.9 70.2Direct 56.5 69.6Graph-based 63.5 74.3ConclusionThis paper presented a graph-based projection approach forcross-lingual SLU using SMTOur approach performed a label propagation algorithm on aproposed graph that was defined with the translations for allover the datasetThe feasibility of our approach was demonstrated by Englishand Korean SLU modelsExperimental results show that our graph-based projectionhelped to improve the performances of the cross-lingual SLUthan previous approaches1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632 Email: kims@i2r.a-star.edu.sg WWW: http://hlt.i2r.a-star.edu.sg/

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