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31st International Conference on Computational Linguistics, COLING 2025

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

ISBN: (纸本)9798891761971

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. ©2025 Association for Computational Linguistics.

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Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings 1

Deep Learning-based Computational Job Market Analysis: A Sur...

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1st Workshop on Natural language processing for Human Resources, NLP4HR 2024

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

ISBN: (纸本)9798891760769

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 Association for Computational Linguistics.

关键词： Terminology

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting 29

DP-Rewrite: Towards Reproducibility and Transparency in Diff...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Transparency

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Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning

arXiv

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arXiv 2024年

作者： Elshabrawy, Ahmed Huang, Yongxin Gurevych, Iryna Aji, Alham Fikri Department Natural Language Processing MBZUAI United Arab Emirates Ubiquitous Knowledge Processing Lab Technical University of Darmstadt Germany

While Large language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Masked language Models (MLMs) like BERT and RoBERTa achieve state-of-the-art results through fine-tuning but struggle with extending to few-shot and zero-shot settings due to their architectural constraints. Hence, we propose Statement-Tuning, a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label. We do Statement-Tuning on multiple tasks to enable cross-task generalization. Experimental results demonstrate that Statement-Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. Moreover, the study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement-Tuning can achieve strong performance with modest training data and benefits from task and statement diversity for unseen task generalizability. We release all the code used to generate statement data, train and evaluate our Statement-Tuned models. © 2024, CC BY.

关键词： Signal encoding

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Zero-shot Sentiment Analysis in Low-Resource languages Using a Multilingual Sentiment Lexicon

arXiv

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arXiv 2024年

作者： Koto, Fajri Beck, Tilman Talat, Zeerak Gurevych, Iryna Baldwin, Timothy Department Natural Language Processing MBZUAI United Arab Emirates Ubiquitous Knowledge Processing Lab Technical University of Darmstadt Germany The University of Melbourne Australia

Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT–3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages. © 2024, CC BY.

关键词： Sentiment analysis

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Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model

Controllable Factuality in Document-Grounded Dialog Systems ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Daheim, Nico Thulke, David Dugast, Christian Ney, Hermann Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Human Language Technology and Pattern Recognition RWTH Aachen University Germany AppTek GmbH

In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes' theorem. One component is a traditional ungrounded response generation model and the other component models the reconstruction of the grounding document based on the dialog context and generated response. We propose different approximate decoding schemes and evaluate our approach on multiple open-domain and task-oriented document-grounded dialog datasets. Our experiments show that the model is more factual in terms of automatic factuality metrics than the baseline model. Furthermore, we outline how introducing scaling factors between the components allows for controlling the tradeoff between factuality and fluency in the model output. Finally, we compare our approach to a recently proposed method to control factuality in grounded dialog, CTRL (Rashkin et al., 2021), and show that both approaches can be combined to achieve additional improvements. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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

arXiv

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arXiv 2024年