Current investigations in data-driven models of parsing have shifted from purely syntactic analysis to richer semantic representations, showing that the successful recovery of the meaning of text requires structured analyses of both its grammar and its semantics. In this article, we report on a joint generative history-based model to predict the most likely derivation of a dependency parser for both syntactic and semantic dependencies, in multiple languages. Because these two dependency structures are not isomorphic, we propose a weak synchronization at the level of meaningful subsequences of the two derivations. These synchronized subsequences encompass decisions about the left side of each individual word. We also propose novel derivations for semantic dependency structures, which are appropriate for the relatively unconstrained nature of these graphs. To train a joint model of these synchronized derivations, we make use of a latent variable model of parsing, the Incremental Sigmoid Belief Network (ISBN) architecture. This architecture induces latent feature representations of the derivations, which are used to discover correlations both within and between the two derivations, providing the first application of ISBNs to a multi-task learning problem. This joint model achieves competitive performance on both syntactic and semantic dependency parsing for several languages. Because of the general nature of the approach, this extension of the ISBN architecture to weakly synchronized syntactic-semantic derivations is also an exemplification of its applicability to other problems where two independent, but related, representations are being learned.

We propose a method for learning dialogue management policies from a fixed data set. The method addresses the challenges posed by Information State Update (ISU)-based dialogue systems, which represent the state of a dialogue as a large set of features, resulting in a very large state space and a huge policy space. To address the problem that any fixed data set will only provide information about small portions of these state and policy spaces, we propose a hybrid model that combines reinforcement learning with supervised learning. The reinforcement learning is used to optimize a measure of dialogue reward, while the supervised learning is used to restrict the learned policy to the portions of these spaces for which we have data. We also use linear function approximation to address the need to generalize from a fixed amount of data to large state spaces. To demonstrate the effectiveness of this method on this challenging task, we trained this model on the COMMUNICATOR corpus, to which we have added annotations for user actions and Information States. When tested with a user simulation trained on a different part of the same data set, our hybrid model outperforms a pure supervised learning model and a pure reinforcement learning model. It also outperforms the hand-crafted systems on the COMMUNICATOR data, according to automatic evaluation measures, improving over the average COMMUNICATOR system policy by 10%. The proposed method will improve techniques for bootstrapping and automatic optimization of dialogue management policies from limited initial data sets.