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This paper describes a method for automatic design of human-computer dialogue strategies by means of reinforcement learning, using a dialogue simulation tool to model the user behaviour and system recognition performance. To the authors' knowledge this is the first application of a detailed simulation tool to this problem. The simulation tool is trained on(More)
This paper analyses the agency explanation for the cross-sectional variation of corporate dividend policy in the UK by looking at the managerial entrenchment hypothesis drawn from the agency literature. Consistent with predictions, a significant U-shaped relationship between dividend payout ratios and insider ownership is observed for a large (exceeding 600(More)
Within all CUED spoken dialogue systems, interactions at the intention level are represented by a core set of dialogue acts. A key feature of the CUED scheme is the provision for representing a distribution of dialogue act hypotheses. To obviate the need for combining multiple acts and the consequent normalisation issues that this would raise, CUED dialogue(More)
Statistical methods have long been the dominant approach in speech recognition and probabilistic modelling in ASR is now a mature technology. The use of statistical methods in other areas of spoken dialogue is however more recent and rather less mature. This paper reviews spoken dialogue systems from a statistical modelling perspective. The complete system(More)
  • LUZI HAIL, CHRISTIAN LEUZ, Holger Daske, Christian Leuz, Rodrigo Verdi, Joachim Gassen +13 others
  • 2013
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. ABSTRACT This study examines liquidity and cost of capital effects around voluntary and mandatory IAS/IFRS adoptions. In contrast to prior work, we focus on the firm-level heterogeneity in the economic consequences, recognizing that firms(More)
This article argues that future generations of computer-based systems will need cognitive user interfaces to achieve sufficiently robust and intelligent human interaction. These cognitive user interfaces will be characterized by the ability to support inference and reasoning, planning under uncertainty, short-term adaptation, and long-term learning from(More)