4 resultados para Expressive language development

em Cambridge University Engineering Department Publications Database


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This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.

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The Pharma(ceuticals) industry is at a cross-roads. There are growing concerns that illegitimate products are penetrating the supply chain. There are proposals in many countries to apply RFID and other traceability technologies to solve this problem. However there are several trade-offs and one of the most crucial is between data visibility and confidentiality. In this paper, we use the TrakChain assessment framework tools to study the US Pharma supply chain and to compare candidate solutions to achieve traceability data security: Point-of-Dispense Authentication, Network-based electronic Pedigree, and Document-based electronic Pedigree. We also propose extensions to a supply chain authorization language that is able to capture expressive data sharing conditions considered necessary by the industry's trading partners. © 2013 IEEE.