Uncertainty in Natural Language Generation: From Theory to Applications

Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications. As such, it is crucial that NLG systems are trustworthy and reliable, for example by indicating when they are likely to be wrong; and supporting multiple views, backgrounds and writing styles -- reflecting diverse human sub-populations. In this paper, we argue that a principled treatment of uncertainty can assist in creating systems and evaluation protocols better aligned with these goals. We first present the fundamental theory, frameworks and vocabulary required to represent uncertainty. We then characterise the main sources of uncertainty in NLG from a linguistic perspective, and propose a two-dimensional taxonomy that is more informative and faithful than the popular aleatoric/epistemic dichotomy. Finally, we move from theory to applications and highlight exciting research directions that exploit uncertainty to power decoding, controllable generation, self-assessment, selective answering, active learning and more

Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data

Better Than Conscious? The Brain, the Psyche, Behavior, and Institutions

The title of this chapter is deliberately provocative. Intuitively, many will be inclined to see conscious control of mental process as a good thing. Yet control comes at a high price. The consciously not directly controlled, automatic, parallel processing of information is not only much faster, it also handles much more information, and it does so in a qualitatively different manner. This different mental machinery is not adequate for all tasks. The human ability to consciously deliberate has evolved for good reason. But on many more tasks than one might think at first sight, intuitive decision-making, or at least an intuitive component in a more complex mental process, does indeed improve performance. This chapter presents the issue, offers concepts to understand it, discusses the effects in terms of problem solving capacity, contrasts norms for saying when this is a good thing, and points to scientific and real world audiences for this work.

An Abductive Argument from Depression and Anxiety to Christian Personal Holiness

The science on pathological depression and anxiety (D&A) describes religion and spirituality\u27s (R/S) insulating and immunizing effects in roughly 72 to 85 percent of all relevant articles. But the descriptivism of science cannot assign any normative value to a theological worldview. Deductive logic favors theism via Leibnizian contingency, Kalām cosmology, objective morality, fine-tuning of the universe, and abstract conceptualism. The information in DNA, the irreducible complexity of intracellular machinery, the improbability of folded proteins, and the support for common modular design over common ancestry establish a design inference for all of life. Unguided naturalistic simulations fail to surmount the complexity barrier of life, diminishing scientific materialism\u27s explanatory power. After philosophical analysis, a cumulative argument using inference to the best explanation (abduction) favors theism over all other worldviews for the complexity of life, the subsequent neurocognitive mechanisms of D&A, and the effects of R/S on D&A. A minimal facts approach establishes Christian theism with positive responses to divine revelation (RDRs) incurring degrees of relative holiness (DRHs). The Christian and non-Christian alike may respond to general revelations in nature and conscience with subsequent DRHs that allow for insulation and immunization against the vicissitudes of life and, therefore, D&A. But the eternal and final solution for D&A is only through a response to the special revelation of the written and living logos with the subsequent imputed holiness of Jesus Christ

The Bayesian boom: good thing or bad?

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition

Design bioethics:a theoretical framework and argument for innovation in bioethics research

Empirical research in bioethics has developed rapidly over the past decade, but has largely eschewed the use of technology-driven methodologies. We propose "design bioethics" as an area of conjoined theoretical and methodological innovation in the field, working across bioethics, health sciences and human-centred technological design. We demonstrate the potential of digital tools, particularly purpose-built digital games, to align with theoretical frameworks in bioethics for empirical research, integrating context, narrative and embodiment in moral decision-making. Purpose-built digital tools can engender situated engagement with bioethical questions; can achieve such engagement at scale; and can access groups traditionally under-represented in bioethics research and theory. If developed and used with appropriate rigor, tools motivated by "design bioethics" could offer unique insights into new and familiar normative and empirical issues in the field.</p

Unconventional Methods for a Traditional Setting: The Use of Virtual Reality to Reduce Implicit Racial Bias in the Courtroom

The presumption of innocence and the right to a fair trial lie at the core of the United States justice system. While existing rules and practices serve to uphold these principles, the administration of justice is significantly compromised by a covert but influential factor: namely, implicit racial biases. These biases can lead to automatic associations between race and guilt, as well as impact the way in which judges and jurors interpret information throughout a trial. Despite the well-documented presence of implicit racial biases, few steps have been taken to ameliorate the problem in the courtroom setting. This Article discusses the potential of virtual reality to reduce these biases among judges and jurors. Through analyzing the various ethical and legal considerations, this Article contends that implementing virtual reality training with judges and jurors would be justifiable and advisable should effective means become available. Given that implicit racial biases can seriously undermine the fairness of the justice system, this Article ultimately asserts that unconventional de-biasing methods warrant legitimate attention and consideration