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Are automatic metrics robust and reliable in specific machine translation tasks? 21

Are automatic metrics robust and reliable in specific machin...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

ISBN: (纸本)9788409019014

We present a comparison of automatic metrics against human evaluations of translation quality in several scenarios which were unexplored up to now. Our experimentation was conducted on translation hypotheses that were problematic for the automatic metrics, as the results greatly diverged from one metric to another. We also compared three different translation technologies. Our evaluation shows that in most cases, the metrics capture the human criteria. However, we face failures of the automatic metrics when applied to some domains and systems. Interestingly, we find that automatic metrics applied to the neural machine translation hypotheses provide the most reliable results. Finally, we provide some advice when dealing with these problematic domains. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Neural machine translation

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A multimodal crowdsourcing framework for transcribing historical handwritten documents 16

A multimodal crowdsourcing framework for transcribing histor...

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16th ACM Symposium on Document Engineering, DocEng 2016

作者： Granell, Emilio Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino Vera s/n 46022 Valencia Spain

ISBN: (纸本)9781450344388

Transcription of handwritten historical documents is one of the main topics in document analysis systems, due to cultural reasons. State-of-the-art handwritten text recognition systems allow to speed up the transcription task. Currently, this automatic transcription is far from perfect, and human expert revision is required in order to obtain the actual transcription. In this context, crowdsourcing emerged as a powerful tool for massive transcription at a relatively low cost, since the supervision effort of professional transcribers may be dramatically reduced. However, current transcription crowdsourcing platforms are mainly limited to the use of nonmobile devices, since the use of keyboards in mobile devices is not friendly enough for most users. This work presents the alternative of using speech dictation of handwritten text lines as transcription source in a crowdsourcing platform. The experiments explore how an initial handwritten text recognition hypothesis can be improved by using the contribution of speech recognition from several speakers, providing as a final result a better hypothesis to be amended by a professional transcriber with less effort.

关键词： Speech recognition

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Creating the best development corpus for Statistical Machine Translation systems 21

Creating the best development corpus for Statistical Machine...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain Sciling València Spain

ISBN: (纸本)9788409019014

We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Machine translation

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Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing 4

Advances on the Transcription of Historical Manuscripts base...

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Granell, Emilio Martínez-Hinarejos, Carlos-D. Romero, Verónica Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camí de Vera s/n València46022 Spain

The transcription of digitalised documents is useful to ease the digital access to their contents. Natural language technologies, such as Automatic Speech recognition (ASR) for speech audio signals and Handwritten Text recognition (HTR) for text images, have become common tools for assisting transcribers, by providing a draft transcription from the digital document that they may amend. This draft is useful when it presents an error rate low enough to make the amending process more comfortable than a complete transcription from scratch. The work described in this thesis is focused on the improvement of the transcription offered by an HTR system from three scenarios: multimodality, interactivity and crowdsourcing. The image transcription can be obtained by dictating their textual contents to an ASR system. Besides, when both sources of information (image and speech) are available, a multimodal combination is possible, and this can be used to provide assistive systems with additional sources of information. Moreover, speech dictation can be used in a multimodal crowdsourcing platform, where collaborators may provide their speech by using mobile devices. Different solutions for each scenario were tested on two Spanish historical manuscripts, obtaining statistically significant improvements. © 4th International Conference, IberSPEECH 2018.

关键词： Crowdsourcing

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Improving Transcription of Manuscripts with Multimodality and Interaction 4

Improving Transcription of Manuscripts with Multimodality an...

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

State-of-the-art Natural language recognition systems allow transcribers to speed-up the transcription of audio, video or image documents. These systems provide transcribers an initial draft transcription that can be corrected with less effort than transcribing the documents from scratch. However, even the drafts offered by the most advanced systems based on Deep Learning contain errors. Therefore, the supervision of those drafts by a human transcriber is still necessary to obtain the correct transcription. This supervision can be eased by using interactive and assistive transcription systems, where the transcriber and the automatic system cooperate in the amending process. Moreover, the interactive system can combine different sources of information in order to improve their performance, such as text line images and the dictation of their textual contents. In this paper, the performance of a multimodal interactive and assistive transcription system is evaluated on one Spanish historical manuscript. Although the quality of the draft transcriptions provided by a Handwriting Text recognition system based on Deep Learning is pretty good, the proposed interactive and assistive approach reveals an additional reduction of transcription effort. Besides, this effort reduction is increased when using speech dictations over an Automatic Speech recognition system, allowing for a faster transcription process. © 4th International Conference, IberSPEECH 2018.

关键词： human computer interaction

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Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles 10

Impact of automatic segmentation on the quality, productivit...

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10th International Conference on language Resources and Evaluation, LREC 2016

作者： Álvarez, Aitor Balenciaga, Marina Del Pozo, Arantza Arzelus, Haritz Matamala, Anna Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech-IK4 San-Sebastian Spain Department of Translation Interpreting and East Asian Studies UAB Barcelona Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782951740891

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

关键词： Productivity

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Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Morpheme-based feature-rich language models using Deep Neura...

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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

作者： El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States

ISBN: (纸本)9781479903566

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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Automatic semantic segmentation of structural elements related to the spinal cord in the lumbar region by using convolutional neural networks 25

Automatic semantic segmentation of structural elements relat...

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25th International Conference on pattern recognition, ICPR 2020

作者： Saenz-Gamboa, Jhon Jairo de la Iglesia-Vayá, Maria Gómez, Jon A. Biomedical Imaging Joint Unit Foundation for the Promotion of Health and Biomedical Research FISABIO-CIPF València Spain Pattern Recognition and Human Language Technology research center Universitat Politècnica de València València Spain

ISBN: (纸本)9781728188089

This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. © 2020 IEEE

关键词： Semantic Segmentation

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The Varied Impacts of Energy Storage and Photovoltaics on Fossil Fuel Emissions

The Varied Impacts of Energy Storage and Photovoltaics on Fo...

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IEEE Power and Energy Society General Meeting

作者： Emily Barrett Brandon Thayer Karen Studarus Seemita Pal Pattern Recognition and Human Language Technology Research Center Universitat Politecnica de Valencia Valencia Spain

ISBN: (纸本)9781538622131;9781538622124

The emissions consequences of smart grid technologies can be significant but are not always intuitive. This is particularly true in the implementation of energy storage (ES) to enable the installation of solar photovoltaic (PV) systems. Using the web-based calculator at and prototypical distribution feeders, this paper explores the CO_2, SO_2 and NO_x impacts of ES deployed with solar PV, where the energy storage system is operated to minimize load variation assuming hourly dispatch. Five regions of the country were explored using 15 prototypical distribution feeders and 2015 historical data. Impacts vary in direction, magnitude, and trend, and require a context-dependent screening method for faithful representation.

关键词： Meteorology Load modeling Fossil fuels Energy storage Smart grids Data models Photovoltaic systems

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Conspiracy vs Critical Thinking Using an Ensemble of Transformers with Data Augmentation Techniques 25

Conspiracy vs Critical Thinking Using an Ensemble of Transfo...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Tulbure, Angelo Maximilian Ardanuy, Mariona Coll Universitat Politècnica de València València Spain Politecnico di Milano Milan Italy Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

This paper provides an overview of our contributions to the PAN at CLEF2024 Oppositional thinking analysis shared task, which focuses on distinguishing between conspiratorial and critical thinking narratives. The competition featured two main tasks. The first task is a binary classification task that aims at determining whether a text is conspiratorial or critical. The second task is a span-level detection task, in which the goal is to detect elements of oppositional narratives in the texts. Two annotated datasets, one in English and one in Spanish, were provided, each of 5K telegram comments. Our best-performing approaches combined custom fine-tuned Transformer models with data augmentation techniques. We achieved an F1-Score of 0.8917 for English and of 0.8293 for Spanish for task 1, and a span-F1 score of 0.6279 for English and 0.6129 for Spanish for task 2. Our task 2 approach achieved the best results in the shared task for both English and Spanish. © 2024 Copyright for this paper by its authors.

关键词： binary classification conspiracy theories critical thinking data augmentation ensembling models oppositional thinking PAN 2024 token classification

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