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How much does Tokenization affect neural machine translation?

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arXiv 2018年

作者： Domingo, Miguel García-Martínez, Mercedes Helle, Alexandre Casacuberta, Francisco Herranz, Manuel Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain Pangeanic / B.I Europa PangeaMT Technologies Division Valencia Spain

Tokenization or segmentation is a wide concept that covers simple processes such as separating punctuation from words, or more sophisticated processes such as applying morphological knowledge. Neural Machine Translation (NMT) requires a limited-size vocabulary for computational cost and enough examples to estimate word embeddings. Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality. Tokenization is more challenging when dealing with languages with no separator between words. In order to assess the impact of the tokenization in the quality of the final translation on NMT, we experimented on five tokenizers over ten language pairs. We reached the conclusion that the tokenization significantly affects the final translation quality and that the best tokenizer differs for different language pairs. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

Sample drop detection for asynchronous devices distributed in space

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Sample drop detection for asynchronous devices distributed i...

European Signal Processing Conference (EUSIPCO)

作者： Tina Raissi Santiago Pascual Maurizio Omologo Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Universitat Politècnica de Catalunya Barcelona Spain Santiago Pascual is currently at Dolby Laboratories Barcelona Spain Center for Information and Communication Technology (ICT) Fondazione Bruno Kessler (FBK) Trento Italy

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drops of samples. In this work, we address the issue of sample drop detection in the context of a conversational speech scenario, recorded by a set of microphones distributed in space. The goal is to design a neural-based model that given a short window in the time domain, detects whether one or more devices have been subjected to a sample drop event. The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step. The latter is based on the application of normalized cross-correlation between signals acquired by different devices. The architecture of the neural network relies on a CNN-LSTM encoder, followed by multi-head attention. The experiments are conducted using both artificial and real data. Our proposed approach obtained F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A comparable performance was found in a larger set of experiments conducted on a set of multi-channel artificial scenes.

关键词： Performance evaluation Array signal processing Speech recognition Synchronization Time-domain analysis Task analysis Microphones

Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding

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arXiv 2024年

作者： Gao, Rong Liu, Xin Xing, Bohao Yu, Zitong Schuller, Bjorn W. Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory School of Engineering Sciences Lappeenranta-Lahti University of Technology LUT Finland School of Computing and Information Technology Great Bay University China Group on Language Audio & Music Imperial College London United Kingdom School of Medicine and Health Technical University of Munich Germany

In this work, we focus on a special group of human body language — the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explored various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient plug-and-play spatiotemporal balancing fusion method. We not only studied our method on the considered micro-gesture dataset but also conducted experiments on mainstream action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on microgestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence. Our code is available at: https://***/ErichG/MG based Emotion *** Codes 68T01 Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

A resource-light method for cross-lingual semantic textual similarity

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arXiv 2018年

作者： Glavaš, Goran Franco-Salvador, Marc Ponzetto, Simone P. Rosso, Paolo Data and Web Science Group School of Business Informatics and Matemathics University of Mannheim B6 26 MannheimDE-68159 Germany Symanto Research Pretzfelder Strasse 15 NurembergDE-90425 Germany Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n ValenciaES-46022 Spain

Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

The INTERSPEECH 2021 computational paralinguistics challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

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arXiv 2021年

作者： Schuller, Björn W. Batliner, Anton Bergler, Christian Mascolo, Cecilia Han, Jing Lefter, Iulia Kaya, Heysem Amiriparian, Shahin Baird, Alice Stappen, Lukas Ottl, Sandra Gerczuk, Maurice Tzirakis, Panagiotis Brown, Chloë Chauhan, Jagmohan Grammenos, Andreas Hasthanasombat, Apinan Spathis, Dimitris Xia, Tong Cicuta, Pietro Rothkrantz, Leon J.M. Zwerts, Joeri Treep, Jelle Kaandorp, Casper GLAM – Group on Language Audio & Music Imperial College London United Kingdom EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Pattern Recognition Lab FAU Erlangen-Nuremberg Germany University of Cambridge United Kingdom Delft University of Technology Netherlands Faculty of Science Utrecht University Netherlands

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech;in the Escalation Sub-Challenge, a three-way assessment of the level of escalation in a dialogue is featured;and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the ‘usual’ COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit;in addition, we add deep end-to-end sequential modelling, and partially linguistic *** Codes 68 © 2021, CC BY-NC-ND.

关键词： Mammals

Integrated system for the efficient maintenance of urban pavements

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Carreteras

Carreteras 2021年第235期4卷 8-18页

作者： Pellicer Armiñana, Eugenio Paredes Palacios, Roberto Felipo Sanjuán, Jesús Sánchez-Robles Bello, Juan Grupo de Gestión del Proceso Proyecto-Construcción Universitat Politécnica de Valencia Spain Centro de Investigación Pattern Recognition and Human Language Technology Universitat Politécnica de Valencia Spain Pavasal Empresa Constructora S.A. Spain Proyectos y Obras. CPS infraestructuras Movilidad y Medio Ambiente S.L. Spain

The integrated system for the efficient maintenance of urban pavements is an innovation project derived from collaboration between a public university and prívate enterprises. This system automates the tasks of auscultation of urban pavements obtaining images with conventional cameras and later processing them, using deep learning techniques. In addition, it Incorporates optimization and predictive decision making that Includes, not only traditional technical criteria (such as the state of the pavement and weather conditions, for example), but also environmental (CO2emissions) and social criteria (proximity to critical infrastructure). The application is based on a friendly visual interface that allows identification and quantification of pavement deterioration in an automatic way, the prediction of the future state of the network, as well as the establishment of an efficient and proactive maintenance and rehabilitation program of urban pavement in the medium-long term. © 2021 Asociacion Espanola de la Carretera. All rights reserved.

关键词： Interface states

An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

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An evolutionary lion optimization algorithm-based image comp...

作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images

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arXiv 2021年

作者： Sáenz-Gamboa, Jhon Jairo Domenech, Julio Alonso-Manjarrés, Antonio Gómez, Jon A. de la Iglesia-Vayá, Maria Av. de Catalunya 21 València46020 Spain Orthopedic Surgery Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Radiology Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camí de Vera s/n València46022 Spain Regional Ministry of Universal Health and Public Health in Valencia Carrer de Misser Mascó 31 València46010 Spain

One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different *** Codes 92B20, 92C50, 68T07, 68T45, 68U10, 92B10 © 2021, CC BY.

关键词： Convolution