Nuclear State Preparation via Landau-Zener-Stueckelberg transitions in Double Quantum Dots

We theoretically model a nuclear-state preparation scheme that increases the coherence time of a two-spin qubit in a double quantum dot. The two-electron system is tuned repeatedly across a singlet-triplet level-anticrossing with alternating slow and rapid sweeps of an external bias voltage. Using a Landau-Zener-Stueckelberg model, we find that in addition to a small nuclear polarization that weakly affects the electron spin coherence, the slow sweeps are only partially adiabatic and lead to a weak nuclear spin measurement and a nuclear-state narrowing which prolongs the electron spin coherence. This resolves some open problems brought up by a recent experiment [D. J. Reilly et al., Science 321, 817 (2008).]. Based on our description of the weak measurement, we simulate a system with up to n=200 nuclear spins per dot. Scaling in n indicates a stronger effect for larger n.Comment: 4.1 pages, 2 figure

Mapping the dialog act annotations of the LEGO corpus into ISO 24617-2 communicative functions

ISO 24617-2, the ISO standard for dialog act annotation, sets the ground for more comparable research in the area. However, the amount of data annotated according to it is still reduced, which impairs the development of approaches for automatic recognition. In this paper, we describe a mapping of the original dialog act labels of the LEGO corpus, which have been neglected, into the communicative functions of the standard. Although this does not lead to a complete annotation according to the standard, the 347 dialogs provide a relevant amount of data that can be used in the development of automatic communicative function recognition approaches, which may lead to a wider adoption of the standard. Using the 17 English dialogs of the DialogBank as gold standard, our preliminary experiments have shown that including the mapped dialogs during the training phase leads to improved performance while recognizing communicative functions in the Task dimension.info:eu-repo/semantics/publishedVersio

Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation

From the perspective of a dialog system, it is important to identify the intention behind the segments in a dialog, since it provides an important cue regarding the information that is present in the segments and how they should be interpreted. ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions which correspond to different intentions that are relevant in the context of a dialog. We explore the automatic recognition of these communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard. To do so, we propose adaptations of existing approaches to flat dialog act recognition that allow them to deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is small, we rely on transfer learning processes to reduce overfitting and improve performance. The results of our experiments show that our approach outperforms both a flat one and hierarchical approaches based on multiple classifiers and that each of its components plays an important role towards the recognition of general-purpose communicative functions.info:eu-repo/semantics/publishedVersio

End-to-end multi-level dialog act recognition

The three-level dialog act annotation scheme of the DIHANA corpus poses a multi-level classification problem in which the bottom levels allow multiple or no labels for a single segment. We approach automatic dialog act recognition on the three levels using an end-to-end approach, in order to implicitly capture relations between them. Our deep neural network classifier uses a combination of word- and character-based segment representation approaches, together with a summary of the dialog history and information concerning speaker changes. We show that it is important to specialize the generic segment representation in order to capture the most relevant information for each level. On the other hand, the summary of the dialog history should combine information from the three levels to capture dependencies between them. Furthermore, the labels generated for each level help in the prediction of those of the lower levels. Overall, we achieve results which surpass those of our previous approach using the hierarchical combination of three independent per-level classifiers. Furthermore, the results even surpass the results achieved on the simplified version of the problem approached by previous studies, which neglected the multi-label nature of the bottom levels and only considered the label combinations present in the corpus.info:eu-repo/semantics/publishedVersio