Semantic annotation of the French media dialog corpus

Bonneau-Maynard, Hélène; Rosset, Sophie; Ayache, Christelle; Kuhn, Anne; Mostefa, Djamel

doi:10.21437/interspeech.2005-312

Cited by 48 publications

(4 citation statements)

References 5 publications

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“…The MEDIA corpus [10] is a French dialogue corpus that simulates a telephone server for tourist information and hotel booking. It has been recorded using a Wizard of Oz technique.…”

Section: Task Description and Semantic Labelingmentioning

confidence: 99%

“…The SLU in the source language is designed for the French MEDIA Corpus [10], which is labeled in terms of concepts, and the target language is Spanish. We have studied two types of SLU techniques: a classical Conditional Random Field (CRF) approach [11] and a Two-level stochastic model [1], which is based on a Stochastic Finite-State Transducers.…”

Section: Introductionmentioning

confidence: 99%

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Combining multiple translation systems for Spoken Language Understanding portability

García

Hurtado

Segarra

et al. 2012

2012 IEEE Spoken Language Technology Workshop (SLT)

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We are interested in the problem of learning Spoken Language Understanding (SLU) models for multiple target languages. Learning such models requires annotated corpora, and porting to different languages would require corpora with parallel text translation and semantic annotations. In this paper we investigate how to learn a SLU model in a target language starting from no target text and no semantic annotation. Our proposed algorithm is based on the idea of exploiting the diversity (with regard to performance and coverage) of multiple translation systems to transfer statistically stable word-toconcept mappings in the case of the romance language pair, French and Spanish. Each translation system performs differently at the lexical level (wrt BLEU). The best translation system performances for the semantic task are gained from their combination at different stages of the portability methodology. We have evaluated the portability algorithms on the French MEDIA corpus, using French as the source language and Spanish as the target language.The experiments show the effectiveness of the proposed methods with respect to the source language SLU baseline.

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“…The MEDIA corpus [10] is a French dialogue corpus that simulates a telephone server for tourist information and hotel booking. It has been recorded using a Wizard of Oz technique.…”

Section: Task Description and Semantic Labelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining multiple translation systems for Spoken Language Understanding portability

García

Hurtado

Segarra

et al. 2012

2012 IEEE Spoken Language Technology Workshop (SLT)

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“…• reported detailed results on ASR tasks in spite of the fact that our decoders have been used to this end. We have even implemented speech preprocessing modules based on [Nadeu et al 1996] and tested with Media [Bonneau-Maynard et al 2005] and with the Spanish monolingual News from WMT2011 [Callison-Burch et al 2011]; 17 • studied or taken into account some entire families or decoder types, notably those based on the static network approach (with is usually based on on-the-fly composition and transducer determinization algorithms) or on stack based decoders (A ). We believe that a comparison with those models would substantially improve the significance of conclusions; • reviewed model estimation.…”

Section: Relating Specific Tasksmentioning

confidence: 99%

“…Maybe, they could be used to perform some kind of concept tagging for SLU or simply use them to construct concepts richer than categories (e.g. dates and place descriptions suitable for a booking dialogue task [Bonneau-Maynard et al 2005]).…”

Section: Some Final Thoughtsmentioning

confidence: 99%

Contributions to the joint segmentation and classification of sequences (My two cents on decoding and handwriting recognition)

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To my family, past, present and future, very specially to my parents and to my wife Manuela for their unconditional love and support. v 65 3.1.8 Prediction/Forecasting 65 3.1.9 Translation 65 3.1.10 Alignment 66 3.1.11 KWS, indexing, querying by example 69 3.1.12 Unsupervised term discovery 72 3.1.13 Filtering/Denoising and Smoothing 73 3.1.14 Summarization 73 3.1.15 Joint Segmentation and Classification 74 3.2 Some extensions 80 3.2.1 Several input sequences 80 3.2.2 Interactive systems 82 xv xvi Contents 3.3 Limitations, how to face them, new proposal 87 3.4 Questionnaire when faced with a new problem 91 3.5 Analysis and evaluation measures 93 3.5.1 Quality assessment 96 3.5.2 Performance 99 3.5.3 Interaction 100 3.5.4 Other measures 102 3.6 Summary and some conclusions 102 105 4.1 Some preliminary ML concepts 106 4.1.1 (Probabilistic) graphical models 115 4.2 Two stage generative model 130 4.2.1 Hierarchy for the second stage 133 4.2.2 Limitations, extensions and generalizations 137 4.3 Classical problems of two stage generative models 141 4.3.1 Probability of observation 142 4.3.2 Decoding 144 4.3.3 Model estimation 145 4.4 Some alternative models 146 4.4.1 Relationship with Dynamic Graphical Models 146 4.4.2 Fixed dimension feature segments 150 4.4.3 Estimation of frame-wise segment posteriors 155 4.4.4 Graph transformer networks 160 4.4.5 Some non-probabilistic frameworks 163 4.5 Summary and some conclusions 165 , , , 167 5.1 Introduction 167 5.2 Recognition, parsing, decoding 170 5.3 Weighted languages and semirings 172 5.4 Some formalisms 177 5.4.1 Formal/generative grammars 177 5.4.2 Finite state automata and transducers 184 5.4.3 Recurrent transition networks 187 5.5 Deriving the composition of a regular and a CF model 194 5.5.1 State-pair transducer composition 195 5.5.2 Extension to null-transitions 200 5.5.3 Extension to model reference transitions 209 5.5.4 Transformation to homogeneous epsilon form 220 5.6 Review of parsing approaches, decoders and algorithms 225 5.7 From composition to recognition/decoding 232 5.7.1 Acyclic inputs 233 5.7.2 Semiring specific optimizations 235 5.8 Summary and some conclusions 235 239 6.1 Probabilistic decompositions 242 6.1.1 Chain rule, clustering histories 243 6.1.2 Whole sentence LMs 244 6.1.3 Combining spans of the sequence 245 6

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Named Entities for Computational Linguistics

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Semantic annotation of the French media dialog corpus

Cited by 48 publications

References 5 publications

Combining multiple translation systems for Spoken Language Understanding portability

Combining multiple translation systems for Spoken Language Understanding portability

Contributions to the joint segmentation and classification of sequences (My two cents on decoding and handwriting recognition)

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