82 research outputs found

    The AMR-PT corpus and the semantic annotation of challenging sentences from journalistic and opinion texts

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    ABSTRACT One of the most popular semantic representation languages in Natural Language Processing (NLP) is Abstract Meaning Representation (AMR). This formalism encodes the meaning of single sentences in directed rooted graphs. For English, there is a large annotated corpus that provides qualitative and reusable data for building or improving existing NLP methods and applications. For building AMR corpora for non-English languages, including Brazilian Portuguese, automatic and manual strategies have been conducted. The automatic annotation methods are essentially based on the cross-linguistic alignment of parallel corpora and the inheritance of the AMR annotation. The manual strategies focus on adapting the AMR English guidelines to a target language. Both annotation strategies have to deal with some phenomena that are challenging. This paper explores in detail some characteristics of Portuguese for which the AMR model had to be adapted and introduces two annotated corpora: AMRNews, a corpus of 870 annotated sentences from journalistic texts, and OpiSums-PT-AMR, comprising 404 opinionated sentences in AMR

    Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

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    The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

    A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

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    The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

    20. ASIM Fachtagung Simulation in Produktion und Logistik 2023

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    Assessing the Impact of Climate Change on Urban Cultural Heritage

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    This book is a printed edition of the Special Issue titled “Assessing the Impact of Climate Change on Urban Cultural Heritage” hosted at the Atmosphere journal. This topic has been chosen in light of cities’ ever-growing role and immense potential in the climate adaptation and mitigation discourse and the particular challenges regarding urban heritage making and conservation. It is critical to recognise the complex set of factors governing the physical, social and political future of urban heritage in cityscapes in constant transformation and in an era of planetary urbanisation. The 10 papers (seven research papers, two reviews and one opinion piece) that comprise the issue give a broad cross-section of the issues pertinent to this important topic – accounts on practices and conceptual/methodological improvements in energy retrofit and reuse, risk mapping, urban planning, climate vulnerability assessment, and community engagement by 38 authors from seven countries are used to delineate the implications of current and likely future climates on heritage materials and systems, knowledge and practice gaps, as well as steps that need to be taken to ensure both their safeguarding and their valorisation to achieve climate resiliency

    Semantic consistency in text generation

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    Automatic input-grounded text generation tasks process input texts and generate human-understandable natural language text for the processed information. The development of neural sequence-to-sequence (seq2seq) models, which are usually trained in an end-to-end fashion, pushed the frontier of the performance on text generation tasks expeditiously. However, they are claimed to be defective in semantic consistency w.r.t. their corresponding input texts. Also, not only the models are to blame. The corpora themselves always include examples whose output is semantically inconsistent to its input. Any model that is agnostic to such data divergence issues will be prone to semantic inconsistency. Meanwhile, the most widely-used overlap-based evaluation metrics comparing the generated texts to their corresponding references do not evaluate the input-output semantic consistency explicitly, which makes this problem hard to detect. In this thesis, we focus on studying semantic consistency in three automatic text generation scenarios: Data-to-text Generation, Single Document Abstractive Summarization, and Chit-chat Dialogue Generation, by seeking for the answers to the following research questions: (1) how to define input-output semantic consistency in different text generation tasks? (2) how to quantitatively evaluate the input-output semantic consistency? (3) how to achieve better semantic consistency in individual tasks? We systematically define the semantic inconsistency phenomena in these three tasks as omission, intrinsic hallucination, and extrinsic hallucination. For Data-to-text Generation, we jointly learn a sentence planner that tightly controls which part of input source gets generated in what sequence, with a neural seq2seq text generator, to decrease all three types of semantic inconsistency in model-generated texts. The evaluation results confirm that the texts generated by our model contain much less omissions while maintaining low level of extrinsic hallucinations without sacrificing fluency compared to seq2seq models. For Single Document Abstractive Summarization, we reduce the level of extrinsic hallucinations in training data by automatically introducing assisting articles to each document-summary instance to provide the supplemental world-knowledge that is present in the summary but missing from the doc ument. With the help of a novel metric, we show that seq2seq models trained with as sisting articles demonstrate less extrinsic hallucinations than the ones trained without them. For Chit-chat Dialogue Generation, by filtering out the omitted and hallucinated examples from training set using a newly introduced evaluation metric, and encoding it into the neural seq2seq response generation models as a control factor, we diminish the level of omissions and extrinsic hallucinations in the generated dialogue responses

    Assessing the technologies transforming the logistics Industry in Nelson Mandela Bay

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    Supply chain disruptions such as those emanating from the current Covid 19 pandemic have made the logistics industry to undergo fast and unprecedented change. In such unpredictable times, innovation and technology adoption has emerged as one of the major trends and key solutions to lead the way for the future of logistics. This is also because a successful and efficient logistics strategy requires the use of technology, as well as the strategic value derived from a firm's capacity. This in turn enables firms to recognise possibilities and challenges resulting from technological advancement in order to attain long-term competitiveness. The industry is implementing these technologies with caution in order to provide faster, cheaper, more dependable and long-term supply. This study sought to investigate whether Nelson Mandela Bay (NMB) is keeping up with the current logistics technology trends and systems that are transforming logistics firms in South Africa, the African continent and the rest of the world. To achieve this primary objective, the study also sought to achieve the following sub-objectives, namely: to identify the logistics technologies transforming logistics firms in NMB; to assess the role of logistics technologies affecting logistics firms in NMB. The study also sought to ascertain the challenges of using logistics technologies transforming the logistics firms in NMB; and examine the effect of logistics technology adoption and use on business performance of logistics firms in NMB. The study used an online closed-ended questionnaire distributed via google forms to collect primary data from a sample of 132 respondents across all the logistics firms (which consisted of warehousing, transport and packaging firms) in NMB. The study targeted those respondents involved in the management of the targeted logistics firms. The empirical results show that technology use in logistics firms has advanced a lot to vehicle tracking, packaging, inventory control, and communication systems, as well as robotics among logistics firms in NMB. The results also identified logistics technologies such as the Internet of Things, Robotic Process automation, Digital Supply Chain Twins, Vendor managed system and RFID as some of the major technologies currently transforming the logistics firms in NMB. The study found that many challenges exist with logistics technology adoption, and cited lack of investment towards logistics technology; the fear of iii losing jobs as people get replaced by technology such as machines and robots; high logistics costs as some of the major challenges. More so, the study results reveal that logistics technology adoption and use play a positive and significant role in logistics firms. The study further reports a significant and positive effect of logistics technology adoption and use on business performance of logistics firms. This study concludes that though still at infancy stage, logistics firms in NMB are keeping upbreast with the current logistics technological trends. The study suggests that firms need to speed up the adoption of the needed logistics technologies available to their respective business in order to remain efficient and effective.Thesis (MA) --Faculty of Business and Economic science, 202

    Graph-based Approaches to Text Generation

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    Deep Learning advances have enabled more fluent and flexible text generation. However, while these neural generative approaches were initially successful in tasks such as machine translation, they face problems – such as unfaithfulness to the source, repetition and incoherence – when applied to generation tasks where the input is structured data, such as graphs. Generating text from graph-based data, including Abstract Meaning Representation (AMR) or Knowledge Graphs (KG), is a challenging task due to the inherent difficulty of properly encoding the input graph while maintaining its original semantic structure. Previous work requires linearizing the input graph, which makes it complicated to properly capture the graph structure since the linearized representation weakens structural information by diluting the explicit connectivity, particularly when the graph structure is complex. This thesis makes an attempt to tackle these issues focusing on two major challenges: first, the creation and improvement of neural text generation systems that can better operate when consuming graph-based input data. Second, we examine text-to-text pretrained language models for graph-to-text generation, including multilingual generation, and present possible methods to adapt these models pretrained on natural language to graph-structured data. In the first part of this thesis, we investigate how to directly exploit graph structures for text generation. We develop novel graph-to-text methods with the capability of incorporating the input graph structure into the learned representations, enhancing the quality of the generated text. For AMR-to-text generation, we present a dual encoder, which incorporates different graph neural network methods, to capture complementary perspectives of the AMR graph. Next, we propose a new KG-to-text framework that learns richer contextualized node embeddings, combining global and local node contexts. We thus introduce a parameter-efficient mechanism for inserting the node connections into the Transformer architecture operating with shortest path lengths between nodes, showing strong performance while using considerably fewer parameters. The second part of this thesis focuses on pretrained language models for text generation from graph-based input data. We first examine how encoder-decoder text-to-text pretrained language models perform in various graph-to-text tasks and propose different task-adaptive pretraining strategies for improving their downstream performance. We then propose a novel structure-aware adapter method that allows to directly inject the input graph structure into pretrained models, without updating their parameters and reducing their reliance on specific representations of the graph structure. Finally, we investigate multilingual text generation from AMR structures, developing approaches that can operate in languages beyond English
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