Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201

Shell nouns : in a systemic functional linguistics perspective

Tese de doutoramento, Linguística (Análise do Discurso), Universidade de Lisboa, Faculdade de Letras, 2015Shell nouns in a Systemic Functional Linguistics perspective. The aim of this thesis is to develop an account of shell nouns (Schmid, 2000) in a Systemic Functional Linguistics (SFL) perspective. Using a parallel corpus comprising five article submissions by Portuguese academics in the field of economics and five published articles on comparable topics, the ideational, interpersonal and textual functions of shell nouns are tagged at the strata of the lexicogrammar and discourse semantics using Corpus Tool version 2.7.4 (O’Donnell, 2008). The systems networks used to tag the corpus are grounded in SFL theory. The analysis shows that shell nouns constitute an important systemic resource for the writers of research articles, who need to build an argument, positioning themselves and their study to convince the discourse community that their paper makes a contribution to knowledge in their disciplinary field. They enable a text to unfold by compacting information realised as a clause or more elsewhere in the text. Thus they can help scaffold a text through hyper-Themes, hyper-News and internal conjunction. At the stratum of the lexicogrammar, anaphorically referring nominal groups with a shell noun as Head often compose Theme, where they constitute a shared point of departure for the clause. In a decoding relational clause whose Process is realised by a verb such as reveal, confirm, or suggest, an anaphorically referring shell noun that construes Token helps to explicitly build the writer’s argument. Shell nouns that construe the field of research, such as results and findings are common in this function. Mental, linguistic and factual shell nouns contribute to construing dialogic position, and coupling between interpersonal systems and textual systems enables the writer to align the reader with certain positions and disalign with others. Although most shell nouns are not field specific, because they can project a figure that instantiates an entity, they contribute to construing field, for example instantiating entities as the object of study of the empirical research. The capacity of shell nouns to function as described above derives from their status as semiotic abstractions, which can refer to text as fact or report and are grammatical metaphors. They can be seen as lying at the intersection of modality and the logico-semantic relations of projection and expansion, brought into being by the semogenic process of nominalisation. The writers of the published articles and article submissions are found to use shell nouns in all of the functions above, but there are differences in the relative shares of the functions, which may affect reader reactions to the text

Incremental Query Generatio

International audienceWe present a natural language generation system which supports the incremental specification of ontology-based queries in natural language. Our contribution is two fold. First, we introduce a chart based surface realisation algorithm which supports the kind of incremental processing required by ontology-based querying. Crucially, this algorithm avoids confusing the end user by preserving a consistent ordering of the query elements throughout the incremental query formulation process. Second, we show that grammar based surface realisation better supports the generation of fluent, natural sounding queries than previous template-based approaches

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

A new low-cost technique improves weather forecasts across the world

Computer-generated forecasts divide the earth's surface into gridboxes, each now ~25% of the size of London, and predict one value per gridbox. If weather varies markedly within a gridbox forecasts for specific sites inevitably fail. A completely new statistical post-processing method, using ensemble forecasts as input, anticipates two gridbox-weather-dependant factors: degree of variation in each gridbox, and bias on the gridbox scale. Globally, skill improves substantially; for extreme rainfall, for example, useful forecasts extend 5 days ahead. Without post-processing this limit is < 1 day. Relative to historical forecasting advances this constitutes ground-breaking progress. The key drivers, incorporated during calibration, are meteorological understanding and abandoning classical notions that only local data be used. Instead we simply recognise that "showers are showers, wherever they occur worldwide" which delivers a huge increase in calibration dataset size. Numerous multi-faceted applications include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.Comment: 27 pages, 9 figures. Submitted to Natur

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

An investigation of a turbulent spray flame using Large Eddy Simulation with a stochastic breakup model

A computational investigation of a turbulent methanol/air spray flame in which a poly-dispersed droplet distribution is achieved through the use of a pressure-swirl atomiser (also known as a simplex atomiser) is presented. A previously formulated stochastic approach towards the modelling of the breakup of droplets in the context of Large Eddy Simulation (LES) is extended to simulate methanol/air flames arising from simplex atomisers. Such atomisers are frequently used to deliver fine droplet distributions in both industrial and laboratory configurations where they often operate under low-pressure drop conditions. The paper describes improvements to the breakup model that are necessary to correctly represent spray formation from simplex atomisers operated under low-pressure drop conditions. The revised breakup model, when used together with the existing stochastic models for droplet dispersion and evaporation, is shown to yield simulated results for a non-reacting spray that agree well with the experimentally measured droplet distribution, spray dynamics and size-velocity correlation. The sub-grid scale (sgs) probability density function (pdf) approach in conjunction with the Eulerian stochastic field method are employed to represent the unknown interaction between turbulence and chemistry at the sub-filter level while a comprehensive kinetics model for methanol oxidation with 18 chemical species and 84 elementary steps is used to account for the gas-phase reaction. A qualitative comparison of the simulated OH images to those obtained from planar laser-induced fluorescence (PLIF) confirms that the essential features of this turbulent spray flame are well captured using the pdf approach. They include the location of the leading-edge combustion (or lift-off height) and the formation of a double reaction zone due to the polydisperse spray. In addition, the influence of the spray flame on the structure of the reacting spray in respect of the mean droplet diameters and spray velocities is reproduced to a good level of accuracy