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Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.

Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing.

In order to build efficient deep recurrent neural architectures, it is essential to analyze the complexityof long distance dependencies (LDDs) of the dataset being modeled. In this paper, we presentdetailed analysis of the dependency decay curve exhibited by various datasets. The datasets sampledfrom a similar process (e.g. natural language, sequential MNIST, Strictlyk-Piecewise languages,etc) display variations in the properties of the dependency decay curve. Our analysis reveal thefactors resulting in these variations; such as (i) number of unique symbols in a dataset, (ii) size ofthe dataset, (iii) number of interacting symbols within a given LDD, and (iv) the distance betweenthe interacting symbols. We test these factors by generating synthesized datasets of the Strictlyk-Piecewise languages. Another advantage of these synthesized datasets is that they enable targetedtesting of deep recurrent neural architectures in terms of their ability to model LDDs with differentcharacteristics. We also demonstrate that analysing dependency decay curves can inform the selectionof optimal hyper-parameters for SOTA deep recurrent neural architectures. This analysis can directlycontribute to the development of more accurate and efficient sequential models.

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition., 18 pages, 1 figure, Appears in CLASP Papers in Computational Linguistics Vol. 1: Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017)

An urgent limitation in current Image Captioning models is their tendency to produce generic captions that avoid the interesting detail which makes each image unique. To address this limitation, we propose an approach that enforces a stronger alignment between image regions and specific segments of text. The model architecture is composed of a visual region proposer, a region-order planner and a region-guided caption generator. The region-guided caption generator incorporates a novel information gate which allows visual and textual input of different frequencies and dimensionalities in a Recurrent Neural Network.

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which contributes to this discrepancy. In order to explain this anomaly, we shed light on the underlying assumption that human mobility characteristics follow an exponential decay that has resulted in this bias. By evaluating the predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, bearing resemblance to power-law decay, contrasting with the initial Markovian assumption. We experimentally validate that this assumption inflates the estimated mobility entropy, consequently lowering the upper bound on predictability. We demonstrate that the existing approach of entropy computation tends to overlook the presence of long-distance dependencies and structural correlations in human mobility. We justify why recurrent-neural network architectures that are designed to handle long-distance dependencies surpass the previously computed upper bound on mobility predictability.

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and modalities of data analytics there is a form of computational stupidity or functional stupidity at work. The exaggerated promises of big data to solve everything are overblown expectations which will lead ultimately to a form of disillusionment with data science. This can be seen in a number of domains, for example smart city technologies, the internet of things, and machine translation. In addition to the negative effects of exaggerated claims of Big Data is the possibility that societal norms will facilitate Big Data technological change by incorporating the bêtise of Big Data, thus leading to a change in our relationship to technology, examples of this would be privacy standards and ownership of data. This paper will conclude by setting out the analysis some of the limitations of Artificial intelligence and Big Data in order to allow a re-examination of the claims made.

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.

This paper examines to what degree current deep learning architectures for image caption generation capture spatial lan- guage. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the cap- tions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric rela- tions between objects.

Dialogue State Tracking is an important task in dialogue management as it provides a mechanism to monitor dialogue contributions. In this paper we introduce an Incremental Joint Model as a new approach to the task. Our tracker is capable of incrementally tracking Dialogue States. We base our model and analysis on the datasets provided in the Second Dialogue State Tracking Challenge (DSTC2). Our early stage evaluations are based on comparisons of our tracker with both the baseline model provide by the DSTC2 and also LecTrack: a state-of-the-art incremental LSTM-based tracker. The main finding of our experiments is that moving from an utterance based to incremental word based tracker results in better performance for our RNN based joint task models.

The aim of this paper is to present techniques and results for identifying the key of Irish traditional music melodies, or tunes. Several corpora are used, consisting of both symbolic and audio representations. Monophonic and heterophonic recordings are present in the audio datasets. Some particularities of Irish traditional music are discussed, notably its modal nature. New key-profiles are defined, that are better suited to Irish music.

In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.

This paper presents an interactive visualisation tool that encodes the scope chain, and information related to the scope chain, within source code. The main goal of the tool is to support programmers when dealing with issues related to scope and to provide answers to questions such as to which scope does a specific variable or function belong to and can I access a specific variable from the scope I am currently located in. The design guidelines followed during the implementation of the tool, as well as the design rationale behind the main features of the tool are described. Finally, the results of a pilot user experience evaluation study are presented where an interesting observation was that the tool seemed to support programmers in verifying and correcting their assumptions when asked questions about specific scoping issues within a source code document.