377,009 research outputs found
Can Beliefs Wrong?
We care what people think of us. The thesis that beliefs wrong, although compelling, can sound ridiculous. The norms that properly govern belief are plausibly epistemic norms such as truth, accuracy, and evidence. Moral and prudential norms seem to play no role in settling the question of whether to believe p, and they are irrelevant to answering the question of what you should believe. This leaves us with the question: can we wrong one another by virtue of what we believe about each other? Can beliefs wrong? In this introduction, I present a brief summary of the articles that make up this special issue. The aim is to direct readers to open avenues for future research by highlighting questions and challenges that are far from being settled. These papers shouldnβt be taken as the last word on the subject. Rather, they mark the beginning of a serious exploration into a set of questions that concern the morality of belief, i.e., doxastic morality
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λ₯μ λν΄μ ν¨κ³Όμ μΈ κ²μ λ°νλ€.Abstract i
Contents vi
List of Tables viii
List of Figures xii
Chapter 1 Introduction 1
Chapter 2 Literature Review 7
2.1 Related Works on Transformer . . . . . . . . . . . . . . . . . . . . . 7
2.2 Related Works on Visual IQ Tests . . . . . . . . . . . . . . . . . . . 10
2.2.1 RPM-related studies . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Object Detection related studies . . . . . . . . . . . . . . . . 11
2.3 Related works on Dialogue State Tracking . . . . . . . . . . . . . . . 12
2.4 Related Works on Mathematical Question Answering . . . . . . . . . 14
2.4.1 Pre-training of Neural Networks . . . . . . . . . . . . . . . . 14
2.4.2 Language Model Pre-training . . . . . . . . . . . . . . . . . . 15
2.4.3 Mathematical Reasoning with Neural Networks . . . . . . . . 17
Chapter 3 Hierarchical end-to-end architecture of Transformer encoders for solving visual IQ tests 19
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.1 Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 Perception Module: Object Detection Model . . . . . . . . . 24
3.2.2 Reasoning Module: Hierarchical Transformer Encoder . . . . 26
3.2.3 Contrasting Module and Loss function . . . . . . . . . . . . . 29
3.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.3 Results for Perception Module . . . . . . . . . . . . . . . . . 35
3.3.4 Results for Reasoning Module . . . . . . . . . . . . . . . . . . 36
3.3.5 Ablation studies . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Chapter 4 Domain-slot relationship modeling using Transformers
for dialogue state tracking 40
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.1 Domain-Slot-Context Encoder . . . . . . . . . . . . . . . . . 44
4.2.2 Slot-gate classifier . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.3 Slot-value classifier . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.4 Total objective function . . . . . . . . . . . . . . . . . . . . . 50
4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.3 Results for the MultiWOZ-2.1 dataset . . . . . . . . . . . . . 52
4.3.4 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Chapter 5 Pre-training of Transformers with Question-Answer Masked
Language Modeling for Mathematical Question Answering 62
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2.1 Pre-training: Question-Answer Masked Language Modeling . 65
5.2.2 Fine-tuning: Mathematical Question Answering . . . . . . . . 67
5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 70
5.3.3 Experimental Results on the Mathematics dataset . . . . . . 71
5.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Chapter 6 Conclusion 79
6.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Bibliography 83
κ΅λ¬Έμ΄λ‘ 101
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Using ontology in query answering systems: Scenarios, requirements and challenges
Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers. L&Cβs company strategy in this area is to design in a step-by-step fashion the essential components of such a system, each component being designed to solve some one part of the total problem and at the same time reflect well-defined needs on the prat of our customers. We compare our strategy with the research roadmap proposed by the Question Answering Committee of the National Institute of Standards and Technology (NIST), paying special attention to the role of ontology
Special Libraries, November 1953
Volume 44, Issue 9https://scholarworks.sjsu.edu/sla_sl_1953/1008/thumbnail.jp
Follow-up question handling in the IMIX and Ritel systems: A comparative study
One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it
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