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1st Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis, KG4IR 2017

作者： Rastogi, Pushpendre Poliak, Adam Van Durme, Benjamin Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

We present ways of incorporating logical rules into the construction of embedding based Knowledge Base Completion (KBC) systems. Enforcing "logical consistency" in the predictions of a KBC system guarantees that the predictions comply with logical rules such as symmetry, implication and generalized transitivity. Our method encodes logical rules about entities and relations as convex constraints in the embedding space to enforce the condition that the score of a logically entailed fact must never be less than the minimum score of an antecedent fact. Such constraints provide a weak guarantee that the predictions made by our KBC model will match the output of a logical knowledge base for many types of logical inferences. We validate our method via experiments on a knowledge graph derived fromWordNet. © Copyright by the paper's authors.

关键词： Forecasting

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On the evaluation of semantic phenomena in neural machine translation using natural language inference

On the evaluation of semantic phenomena in neural machine tr...

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2018

作者： Poliak, Adam Belinkov, Yonatan Glass, James Van Durme, Benjamin Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781948087292

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic *** use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring worldknowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage. © 2018 Association for Computational Linguistics.

关键词： Neural machine translation

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Automatic speech recognition and topic identification for almost-zero-resource languages

arXiv

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arXiv 2018年

作者： Wiesner, Matthew Liu, Chunxi Ondel, Lucas Harman, Craig Manohar, Vimal Trmal, Jan Huang, Zhongqiang Dehak, Najim Khudanpur, Sanjeev Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Brno University of Technology Czech Republic Raytheon BBN Technologies United States

Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search. While universal phone recognition is natural to consider when no transcribed speech is available to train an ASR system in a language, adapting universal phone models using very small amounts (minutes rather than hours) of transcribed speech also needs to be studied, particularly with state-of-the-art DNN-based acoustic models. The DARPA LORELEI program provides a framework for such very-low-resource ASR studies, and provides an extrinsic metric for evaluating ASR performance in a humanitarian assistance, disaster relief setting. This paper presents our Kaldi-based systems for the program, which employ a universal phone modeling approach to ASR, and describes recipes for very rapid adaptation of this universal ASR system. The results we obtain significantly outperform results obtained by many competing approaches on the NIST LoReHLT 2017 Evaluation datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： speech recognition

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Rich morphological tagger for english: Exploring the cross-linguistic tradeoff between morphology and syntax 15

Rich morphological tagger for english: Exploring the cross-l...

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15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Kirov, Christo Sylak-Glassman, John Knowles, Rebecca Cotterell, Ryan Post, Matt Center for Language and Speech Processing United States Department of Computer Science United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781510838604

A traditional claim in linguistics is that all human languages are equally expressive- able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation. © 2017 Association for Computational Linguistics.

关键词： Morphology

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Ethical Research Protocols for Social Media Health Research 1

Ethical Research Protocols for Social Media Health Research

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1st ACL Workshop on Ethics in Natural language processing, EthNLP 2017

作者： Benton, Adrian Coppersmith, Glen Dredze, Mark Center for Language and Speech Processing Johns Hopkins University United States Qntfy United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781945626470

Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research. © 2017 Association for Computational Linguistics.

关键词： Patient treatment

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program

Audio-Visual Person Recognition in Multimedia Data From the ...

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Gregory Sell Kevin Duh David Snyder Dave Etter Daniel Garcia-Romero Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD 21218 USA

ISBN: (纸本)9781538646595

Currently, datasets that support audio-visual recognition of people in videos are scarce and limited. In this paper, we introduce an expansion of video data from the IARPA Janus program to support this research area. We refer to the expanded set, which adds labels for voice to the already-existing face labels, as the Janus Multimedia dataset. We first describe the speaker labeling process, which involved a combination of automatic and manual criteria. We then discuss two evaluation settings for this data. In the core condition, the voice and face of the labeled individual are present in every video. In the full condition, no such guarantee is made. The power of audiovisual fusion is then shown using these publicly-available videos and labels, showing significant improvement over only recognizing voice or face alone. In addition to this work, several other possible paths for future research with this dataset are discussed.

关键词： multimodal audio-visual speaker recognition face recognition multimedia MULTI-MEDIA SYSTEM speaker recognition Facial Recognition Voice Videotapes Dataset Persons video data

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speech enhancement via deep spectrum image translation network

arXiv

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arXiv 2019年

作者： Kashani, Hamidreza Baradaran Goodarzi, Mohammad Mohsen Jodeiri, Ata Rezaei, Iman Sarraf Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptually-modified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Audition

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speech Enhancement via Deep Spectrum Image Translation Network

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Hamidreza Baradaran Kashani Ata Jodeiri Mohammad Mohsen Goodarzi Iman Sarraf Rezaei Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran Research Center for Development of Advanced Technologies Speech and Language Processing Group Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively.

关键词： speech enhancement Noise measurement Feature extraction Training Computer architecture Convolution Frequency-domain analysis

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CADET: Computer Assisted Discovery Extraction and Translation 8

CADET: Computer Assisted Discovery Extraction and Translatio...

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8th International Joint Conference on Natural language processing, IJCNLP 2017

作者： Van Durme, Benjamin Lippincott, Tom Duh, Kevin Burchfield, Deana Poliak, Adam Costello, Cash Finin, Tim Miller, Scott Mayfield, James Koehn, Philipp Harman, Craig Lawrie, Dawn May, Chandler Thomas, Max Carrell, Annabelle Chaloux, Julianne Chen, Tongfei Comerford, Alex Dredze, Mark Glass, Benjamin Hao, Shudong Martin, Patrick Rastogi, Pushpendre Sankepally, Rashmi Wolfe, Travis Tran, Ying-Ying Zhang, Ted Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781948087025

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.1 © 2017 AFNLP

关键词： Extraction

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