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We investigate the problem of estimating the quality of the output of machine translation systems at the sentence level when reference translations are not available. The focus is on automatically identifying a threshold to map a continuous predicted score into " good " / " bad " categories for filtering out bad-quality cases in a translation post-edition(More)
This paper presents a generic dialogue state tracker that maintains beliefs over user goals based on a few simple domain-independent rules, using basic probability operations. The rules apply to observed system actions and partially observable user acts, without using any knowledge obtained from external resources (i.e. without requiring training data). The(More)
We present a novel machine translation framework based on kernel regression techniques. In our model, the translation task is viewed as a string-to-string mapping , for which a regression type learning is employed with both the source and the target sentences embedded into their kernel induced feature spaces. We report the experiments on a French-English(More)
The novel kernel regression model for SMT only demonstrated encouraging results on small-scale toy data sets in previous works due to the complexities of kernel methods. It is the first time results based on the real-world data from the shared translation task will be reported at ACL 2008 Workshop on Statistical Machine Translation. This paper presents the(More)
We describe a variety of machine-learning techniques that are being applied to social multiuser human--robot interaction using a robot bartender in our scenario. We first present a data-driven approach to social state recognition based on <i>supervised learning</i>. We then describe an approach to social skills execution&#8212;that is, action selection for(More)
This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multi-domain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction(More)
This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented , which leads to more natural dialogues. The use of the power set results in a massive(More)
Managing multimodal interactions between humans and computer systems requires a combination of state estimation based on multiple observation streams, and optimisation of time-dependent action selection. Previous work using partially observable Markov decision processes (POMDPs) for multimodal interaction has focused on simple turn-based systems. However,(More)