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ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain

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arXiv 2023年

作者： Zhang, Mike van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark MaiNLP Center for Information and Language Processing LMU Munich Germany

The increasing number of benchmarks for Natural language processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-Rlarge, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-Rlarge on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level. © 2023, CC BY.

关键词： Taxonomies

Greek Sign language Recognition for the SL-ReDu Learning Platform 7

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Greek Sign Language Recognition for the SL-ReDu Learning Pla...

7th Workshop on Sign language Translation and Avatar Technology: The Junction of the Visual and the Textual Challenges and Perspectives, SLTAT 2022

作者： Papadimitriou, Katerina Potamianos, Gerasimos Sapountzaki, Galini Goulas, Theodor Efthimiou, Eleni Fotinea, Stavroula-Evita Maragos, Petros Department of Electrical & Computer Engineering University of Thessaly Volos Greece Department of Special Education University of Thessaly Volos Greece Institute for Language & Speech Processing Athena Research & Innovation Center Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9791095546825

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively. © European language Resources Association (ELRA), licensed under CC-BY-NC 4.0.

关键词： Deep learning

CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary Continuous Visual to speech Synthesis

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CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary...

International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Chen Chen Dong Wang Thomas Fang Zheng Center for Speech and Language Technologies BNRist Tsinghua University China Department of Computer Science and Technology Tsinghua University China

Research on Video to speech Synthesis (VTS) surges recently and the focus is gradually shifting from small-vocabulary short-phrase VTS to large-vocabulary continuous VTS (LVC-VTS). A large-scale dataset with sufficient speakers and utterances is a prerequisite for such research, and the database is certainly language *** this paper, we introduce CN-CVS, a large-scale Mandarin continuous visual-speech dataset, to support LVC-VTS research. The dataset contains about 200k utterances from more than 2500 individuals, amounting to more than 300 hours of visual-speech data. We built a state-of-the-art VTS model with the new dataset and conducted preliminary studies. Our results show that models that achieve good performance on small vocabulary tasks may perform very poor on CN-CVS, indicating that continuous VTS is indeed a challenging task, and the main challenge comes from the unconstrained vocabulary. The dataset and baseline code can be downloaded for free from http://***.

关键词： Vocabulary Visualization Codes Signal processing Cameras speech synthesis Visual databases

Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson's disease 46

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Concurrent validity of instrumented insoles measuring gait a...

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Watkinson, Sophia A. Anderson, Anthony Caiola, Michael Eguren, David Gonzalez, Michael Velazquez, Laureano Moro Dehak, Najim Motley, Chelsea Moukheiber, Emile Mills, Kelly Muir, Brittney Butala, Ankur Kontson, Kimberly Us Food and Drug Administration Division of Biomedical Physics Silver SpringMD United States Johns Hopkins University Center for Language and Speech Processing BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University School of Medicine Department of Neurology BaltimoreMD United States Center for Limb Loss and Mobility Department of Veterans Affairsy SeattleWA United States

ISBN: (纸本)9798350371499

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M;mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson's disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson's correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems. © 2024 IEEE.

关键词： Gait analysis

Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties

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arXiv 2024年

作者： Artemova, Ekaterina Blaschke, Verena Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark Toloka.AI

Mainstream cross-lingual task-oriented dialogue (ToD) systems leverage the transfer learning paradigm by training a joint model for intent recognition and slot-filling in English and applying it, zero-shot, to other languages. We address a gap in prior research, which often overlooked the transfer to lower-resource colloquial varieties due to limited test data. Inspired by prior work on English varieties, we craft and manually evaluate perturbation rules that transform German sentences into colloquial forms and use them to synthesize test sets in four ToD datasets. Our perturbation rules cover 18 distinct language phenomena, enabling us to explore the impact of each perturbation on slot and intent performance. Using these new datasets, we conduct an experimental evaluation across six different transformers. Here, we demonstrate that when applied to colloquial varieties, ToD systems maintain their intent recognition performance, losing 6% (4.62 percentage points) in accuracy on average. However, they exhibit a significant drop in slot detection, with a decrease of 31% (21 percentage points) in slot F1 score. Our findings are further supported by a transfer experiment from Standard American English to synthetic Urban African American Vernacular English. © 2024, CC BY.

关键词： Zero-shot learning

Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static language Models

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Methodological Insights in Detecting Subtle Semantic Shifts ...

2023 Findings of the Association for Computational Linguistics: EMNLP 2023

作者： Hoeken, Sanne Alaçam, Özge Fokkens, Antske Sommerauer, Pia Computational Linguistics Dept. of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9798891760615

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction which relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts. © 2023 Association for Computational Linguistics.

关键词： Semantics

Predicting Early Dropouts of an Active and Healthy Ageing App

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arXiv 2023年

作者： Perifanis, Vasileios Michailidi, Ioanna Stamatelatos, Giorgos Drosatos, George Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022 (***), part of IUPESM WC 2022. We have processed the given database and generated seven datasets. We used pre-processing techniques to construct classification models that predict the adherence of users using dynamic and static features. We submitted 11 official runs and our results show that machine learning algorithms can provide high-quality adherence predictions. Based on the results, the dynamic features positively influence a model’s classification performance. Due to the imbalanced nature of the dataset, we employed oversampling methods such as SMOTE and ADASYN to improve the classification performance. The oversampling approaches led to a remarkable improvement of 10%. Our methods won first place in the IFMBE Scientific Challenge 2022. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

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arXiv 2024年

作者： Senger, Elena Zhang, Mike van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Department of Computer Science IT University of Copenhagen Denmark Fraunhofer Center for International Management and Knowledge Economy IMW Germany

Recent years have brought significant advances to Natural language processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this emerging field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction and classification. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification. © 2024, CC BY.

关键词： Terminology

KARRIEREWEGE: A Large Scale Career Path Prediction Dataset

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arXiv 2024年

作者： Senger, Elena Campbell, Yuri van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Fraunhofer Center for International Management and Knowledge Economy IMW Germany Department of Computer Science IT University of Copenhagen Denmark

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data. © 2024, CC BY.

关键词： Prediction models