1,095 research outputs found

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    The pharmaco-epidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure and to prognosis

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    Heart failure is a major and growing public health problem associated with poor patient outcomes, including reduced quality of life and high hospitalisation and mortality rates. It is a complex clinical syndrome rather than a single disease, which lacks a practical, universal, and standardised definition. Currently, the definition relies on the identification of symptoms and signs of cardiac dysfunction, such as ankle swelling and breathlessness, which are neither specific nor objective. Many patients are only diagnosed once their symptoms and signs are severe enough to require hospitalisation. Pathophysiologically, heart failure can be defined by the presence of salt and water retention, also known as congestion, associated with cardiac dysfunction. Within the United Kingdom, the pharmacological class of loop diuretics is used primarily for the treatment of congestion due to cardiac dysfunction. The aim of this thesis is to investigate the pharmacoepidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure, with a particular focus on patient outcomes. The first analysis describes the prevalence of repeated loop diuretic dispensing and/or diagnosis of heart failure within the NHS Greater Glasgow & Clyde Health Board population on 1st January 2012, including patient outcomes over the following five years. This research is thought to be the first population-level investigation into the prevalence of repeated loop diuretic dispensing and its prognostic significance in patients with and without a diagnosis of heart failure. The analysis found that an estimated 3.2% of the population received repeated loop diuretic dispensing, while only 1.3% of the population had a diagnosis of heart failure. Hospitalisation rates were higher in those with a loop diuretic (0.99 admissions per patient-year at risk for those with only repeated loop diuretic dispensing and 1.51 admissions per patient-year at risk for those with both) than those with only a diagnosis of heart failure (0.93 admissions patient-year at risk). All-cause mortality followed a similar pattern; adjusting for age, sex, socioeconomic deprivation and comorbidity status, the 5-year hazard ratio and (95% confidence interval) were 1.8 (1.8 - 1.9) for those with those only repeated loop diuretic dispensing and 2.3 (2.2 - 2.4) for those with both, while only 1.2 (2.2 - 2.4) for those with only a diagnosis of heart failure, implying that the presence of repeated loop diuretic dispensing is a marker of serious disease. The second analysis stepped backwards in ‘patient-time’ to describe the pattern of hospitalisations in the year leading up to the initiation of loop diuretic dispensing or an incident diagnosis of heart failure using network graphs. While the precursors to heart failure are known, this research is thought to be the first to report the common patterns in events leading up to the initiation of loop diuretics. While there was little difference in comorbidity and medication levels 24 months prior, in the year leading up to the initiation, those who received a diagnosis of heart failure were more likely to be admitted for well-recognised contributors to the condition, including ischaemic heart disease in particular, but also atrial fibrillation/flutter and valve disease. In contrast, these patterns were not often seen in those who were only initiated on a loop diuretic, instead with a focus on admissions for non-specific symptoms and signs, most commonly unspecified chest pain. The third analysis starts where the second leaves off. It assesses the prognostic relationship between the initiation of loop diuretic and diagnosis of heart failure on mortality and whether the sequence of these events matters using semi-Markov multi-state modes, a flexible model for use on longitudinal time data where there is an event-related dependence on outcomes. Those on repeated loop diuretic dispensing without a diagnosis of heart failure were majority women (62%). Many with evidence of left atrial dilation (53%), while those with a diagnosis of heart failure without a repeat loop diuretic were majority men (63%). Many had a history of myocardial infarction (51%). Hospitalisations and mortality were higher in those with a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.44; mortality, 0.20) compared to those with a diagnosis of heart failure without a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.47; mortality, 0.14). Rates were higher still in those with both loop diuretic and heart failure (where both events occurred together within the first year per patient-year at risk: hospitalisation, 1.74; mortality, 0.16; or where the diagnosis of HF preceded the initiation of loop diuretic, within the first year per patient-year at risk: hospitalisation, 1.68; mortality, 0.20), with the highest being in those who initiated the loop diuretic in advance of receiving a diagnosis of heart failure (within the first year per patient-year at risk: hospitalisation, 2.26; mortality, 0.28). The fourth and final analysis subsets the population to investigate the mortality of the 24,921 patients with ischaemic heart disease according to whether or not they have had a repeat loop diuretic and/or diagnosis of heart failure; of whom, 3,806 had only repeat loop diuretic, 2,384 had only a diagnosis of heart failure, and 3,531 had both. This analysis found that after adjusting for age, sex, and other prognostic markers, mortality was associated with the repeat loop diuretic regardless of the patient’s heart failure status. Those with a repeat loop diuretic without a diagnosis of heart failure experienced substantially higher rates of cardiovascular (an estimated 15%) and all-cause mortality (47%) than those with a diagnosis of heart failure without a repeat loop diuretic (an estimated 8% cardiovascular and 19% all-cause mortality), while rates were highest for those with both (an estimated 25% cardiovascular and 57% all-cause mortality). In conclusion, these analyses found that many more patients are repeatedly treated with loop diuretic than ever receive a diagnosis of heart failure. These patients are at a high risk of hospitalisation and death, and based on their characteristics, many probably have undiagnosed heart failure. From a public health and epidemiological perspective, the current definition of heart failure likely underestimates the true burden on the healthcare system. From the patient’s perspective, with the efficacy of angiotensin receptor-neprilysin inhibitor, sodium-glucose co-transporter-2 inhibitors, and mineralocorticoid receptor antagonistss, a missed diagnosis means a missed opportunity to improve the patient’s outcome and quality of life, regardless of their heart failure phenotype. Even more alarming, if these patients are receiving the loop diuretic inappropriately, the loop diuretic is likely causing these increased hospitalisation and mortality rates. If the loop diuretic can be safely withdrawn, other medications with diuretic properties exist which have good safety profiles. Ultimately, further research is required to determine the optimal strategy for managing these patients

    Arkansas Law Review - Volume 75 Issue 4

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    The Structure of Criminal Federalism

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    Scholars and courts have long assumed that a limited federal government should stick to genuinely “federal” crimes and leave “local” crimes to the states. By that measure, criminal federalism has failed; federal criminal law largely overlaps with state crime, and federal prosecutors regularly do seemingly “local” cases. Despite nearly unlimited paper jurisdiction, however, the federal enforcement footprint has remained tiny and virtually static for a century. Something is strongly limiting the federal system, just not differences in substantive coverage. The answer is different enforcement responsibilities. The police power means states alone provide basic public safety and criminal justice. Rather than inefficiently duplicate that role, the federal system leverages the states’ existing people and infrastructure, supplementing and correcting inevitable enforcement breakdowns. Far from signaling a federalism failure, overlapping law and cooperative enforcement thus powerfully constrain the federal system by keeping it secondary and small. Overlapping criminal enforcement, this Article demonstrates, is deeply rooted in law and tradition. Overlapping enforcement also offers a novel federalism model in which the states are neither separate nor servants but entrenched on the front lines, genuinely cooperating with federal backup to enforce criminal policy. Scholars, courts, and policymakers can and should embrace, rather than resist, the real structure of criminal federalism

    Learned interpreters : structural and learned systematicity in neural networks for program execution

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    Les architectures de rĂ©seaux de neurones profonds Ă  usage gĂ©nĂ©ral ont fait des progrĂšs surprenants dans l'apprentissage automatique pour le code, permettant l’amĂ©lioration de la complĂ©tion de code, la programmation du langage naturel, la dĂ©tection et la rĂ©paration des bogues, et mĂȘme la rĂ©solution de problĂšmes de programmation compĂ©titifs Ă  un niveau de performance humain. NĂ©anmoins, ces mĂ©thodes ont du mal Ă  comprendre le processus d'exĂ©cution du code, mĂȘme lorsqu'il s'agit de code qu'ils Ă©crivent eux-mĂȘmes. À cette fin, nous explorons une architecture du rĂ©seau neuronal inspirĂ© d’interprĂ©teur de code, via une nouvelle famille d'architecture appelĂ©e Instruction Pointer Attention Graph Neural Networks (IPA-GNN). Nous appliquons cette famille d'approches Ă  plusieurs tĂąches nĂ©cessitant un raisonnement sur le comportement d'exĂ©cution du programme : apprendre Ă  exĂ©cuter des programmes complets et partiels, prĂ©dire la couverture du code pour la vĂ©rification du matĂ©riel, et prĂ©dire les erreurs d'exĂ©cution dans des programmes de compĂ©tition. GrĂące Ă  cette sĂ©rie de travaux, nous apportons plusieurs contributions et rencontrons de multiples rĂ©sultats surprenants et prometteurs. Nous introduisons une bibliothĂšque Python pour construire des reprĂ©sentations de graphes des programmes utiles dans la recherche sur l'apprentissage automatique, qui sert de fondement Ă  la recherche dans cette thĂšse et dans la communautĂ© de recherche plus large. Nous introduisons Ă©galement de riches ensembles de donnĂ©es Ă  grande Ă©chelle de programmes annotĂ©s avec le comportement du programme (les sorties et les erreurs soulevĂ©es lors de son exĂ©cution) pour faciliter la recherche dans ce domaine. Nous constatons que les mĂ©thodes IPA-GNN prĂ©sentent une forte gĂ©nĂ©ralisation amĂ©liorĂ©e par rapport aux mĂ©thodes Ă  usage gĂ©nĂ©ral, fonctionnant bien lorsqu'ils sont entraĂźnĂ©s pour exĂ©cuter uniquement des programmes courts mais testĂ©s sur des programmes plus longs. En fait, nous constatons que les mĂ©thodes IPA-GNN surpassent les mĂ©thodes gĂ©nĂ©riques sur chacune des tĂąches de modĂ©lisation du comportement que nous considĂ©rons dans les domaines matĂ©riel et logiciel. Nous constatons mĂȘme que les mĂ©thodes inspirĂ©es par l'interprĂ©teur de code qui modĂ©lisent explicitement la gestion des exceptions ont une propriĂ©tĂ© interprĂ©tative souhaitable, permettant la prĂ©diction des emplacements d'erreur mĂȘme lorsqu'elles n'ont Ă©tĂ© entraĂźnĂ©es qu'Ă  prĂ©dire la prĂ©sence d'erreur et le type d'erreur. Au total, les architectures inspirĂ©es des interprĂ©teurs de code comme l'IPA-GNN reprĂ©sentent un chemin prometteur Ă  suivre pour imprĂ©gner des rĂ©seaux de neurones avec de nouvelles capacitĂ©s pour apprendre Ă  raisonner sur les exĂ©cutions de programme.General purpose deep neural network architectures have made startling advances in machine learning for code, advancing code completion, enabling natural language programming, detecting and repairing bugs, and even solving competitive programming problems at a human level of performance. Nevertheless, these methods struggle to understand the execution behavior of code, even when it is code they write themselves. To this end, we explore interpreter-inspired neural network architectures, introducing a novel architecture family called instruction pointer attention graph neural networks (IPA-GNN). We apply this family of approaches to several tasks that require reasoning about the execution behavior of programs: learning to execute full and partial programs, code coverage prediction for hardware verification, and predicting runtime errors in competition programs. Through this series of works we make several contributions and encounter multiple surprising and promising results. We introduce a Python library for constructing graph representations of programs for use in machine learning research, which serves as a bedrock for the research in this thesis and in the broader research community. We also introduce rich large scale datasets of programs annotated with program behavior like outputs and errors raised to facilitate research in this domain. We find that IPA-GNN methods exhibit improved strong generalization over general purpose methods, performing well when trained to execute only on short programs and tested on significantly longer programs. In fact, we find that IPA-GNN methods outperform generic methods on each of the behavior modeling tasks we consider across both hardware and software domains. We even find that interpreter-inspired methods that model exception handling explicitly have a desirable interpretability property, enabling the prediction of error locations even when only trained on error presence and kind. In total, interpreter-inspired architectures like the IPA-GNN represent a promising path forward for imbuing neural networks with novel capabilities for learning to reason about program executions

    Gurus and Media: Sound, image, machine, text and the digital

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    Gurus and Media is the first book dedicated to media and mediation in domains of public guruship and devotion. Illuminating the mediatisation of guruship and the guru-isation of media, it bridges the gap between scholarship on gurus and the disciplines of media and visual culture studies. It investigates guru iconographies in and across various time periods and also the distinctive ways in which diverse gurus engage with and inhabit different forms of media: statuary, games, print publications, photographs, portraiture, films, machines, social media, bodies, words, graffiti, dolls, sound, verse, tombs and more. The book’s interdisciplinary chapters advance, both conceptually and ethnographically, our understanding of the function of media in the dramatic production of guruship, and reflect on the corporate branding of gurus and on mediated guruship as a series of aesthetic traps for the captivation of devotees and others. They show how different media can further enliven the complex plurality of guruship, for instance in instantiating notions of ‘absent-present’ guruship and demonstrating the mutual mediation of gurus, caste and Hindutva. Throughout, the book foregrounds contested visions of the guru in the development of devotional publics and pluriform guruship across time and space. Thinking through the guru’s many media entanglements in a single place, the book contributes new insights to the study of South Asian religions and to the study of mediation more broadly

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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