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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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CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary Continuous Visual to speech Synthesis 48

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

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48th IEEE International Conference on Acoustics, speech and Signal processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

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://***. © 2023 IEEE.

关键词： audio-visual dataset Mandarin video to speech synthesis

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Multi-low resource languages in palm leaf manuscript recognition: Syllable-based augmentation and error analysis

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Pattern Recognition Letters 2025年 195卷 8-15页

作者： Thuon, Nimol Du, Jun Theang, Panhapin Thuon, Ranysakol National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China School of Public Affairs University of Science and Technology of China Hefei Anhui China Research Innovation Department One to Many Cambodia Phnom Penh Cambodia Department of Geological Engineering Universitas Gadjah Mada Yogyakarta Indonesia

Recognizing text from palm leaf manuscripts in low-resource, non-Latin languages like Balinese, Khmer, and Sundanese poses significant challenges due to limited annotated data and complex structures. Unlike modern languages, these ancient scripts exhibit unique linguistic complexities that hinder effective recognition and digital preservation. Building on the success of syllable analysis augmentation for the Khmer script, we propose a framework, PALM-SADA, for multi-script recognition. PALM-SADA integrates visual and linguistic processing using a hybrid CNN-Transformer architecture. The framework introduces syllable analysis augmentation techniques, consisting of two main components. (1) Monosyllabic synthesis generates single-syllable words by combining glyphs from isolated glyph datasets using predefined grammar forms. And (2) Polysyllabic synthesis creates longer, grammatically correct text sequences by combining monosyllabic words and isolated glyphs. To ensure linguistic integrity, grammar forms and vocabulary lists of complete words were meticulously designed and validated, preserving the linguistic characteristics of the augmented data. For recognition, PALM-SADA employs a hybrid CNN-Transformer network that enhances both feature extraction and transcription accuracy. CNN layers capture local features, while Transformer layers model global dependencies. A Transformer-based decoder further refines transcriptions by leveraging contextual relationships within the text. Experiments conducted on the ICFHR 2018 contest datasets demonstrate that PALM-SADA significantly outperforms existing methods. © 2025 Elsevier B.V.

关键词： Semantics

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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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English to Arabic Braille Neural Machine Translation Through Corpus Augmentation 6

English to Arabic Braille Neural Machine Translation Through...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Joshi, Nisheeth Katyayan, Pragya Ahmed, Syed Afroz Speech and Language Processing Lab Center for Artificial Intelligence Vidyapith Rajasthan Banasthali India School of Computer Science University of Petroleum and Energy Studies Utttrakhand India Umm Al Quwain University United Arab Emirates

In this paper we have shown the development of English to Arabic Braille Neural Machine Translation (NMT) System. For our experiments we have developed two NMT systems. The first was the baseline NMT system which was trained only on the English-Arabic parallel corpus whereas the second NMT system has some sub modules for handling syntax transfer of English to Arabic and translation/transliteration of English Named Entities into Arabic equivalents. Then the system used this code-mixed text to train the NMT model. The English-Arabic parallel corpus was taken from Opus parallel corpus repository. For preprocessing of the English text Stanford's Stanza NLP Library was used. The trained NMT models were evaluated using standard automatic MT evaluation metrics and their results were also corelated with human judgements. It was found that the corpus augmented NMT system performed better than the baseline NMT system. © 2024 The Authors. Published by Elsevier B.V.

关键词： Neural machine translation

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Known Words Will Do: Unknown Concept Translation via Lexical Relations 5

Known Words Will Do: Unknown Concept Translation via Lexical...

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5th Workshop on Technologies for Machine Translation of Low-Resource languages, LoResMT 2022 at 29th International Conference on Computational Linguistics. COLING 2022

作者： Wu, Winston Yarowsky, David Computer Science and Engineering University of Michigan United States Center for Language and Speech Processing Johns Hopkins University United States

Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of specialized terms into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach. © 2022 COLING. All Rights Reserved.

关键词： Translation (languages)

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Isarn Dialect Word Segmentation using Bi-directional Gated Recurrent Unit with transfer learning approach 26

Isarn Dialect Word Segmentation using Bi-directional Gated R...

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26th International computer science and Engineering Conference, ICSEC 2022

作者： Aim-Nang, Sawetsit Seresangtakul, Pusadee Janyoi, Pongsathon College of Computing Khon Kaen University Natural Language and Speech Processing Laboratory Department of Computer Science Khon Kaen Thailand

ISBN: (纸本)9781665491983

This paper presents an Isarn dialect word segmentation based on a recurrent neural network. In this study, the Isarn text written in Thai script is taken as input. We explored the effectiveness of the types of recurrent layers;recurrent neural networks (RNN), gated recurrent units (GRU), and long short-term memory (LSTM). The F1-scores of RNN, GRU, and LSTM are 95.36, 96.05, and 95.70, respectively. The experiment results showed that using GRU as the recurrent layer achieved the best performance. To deal with borrowed words from Thai, transfer learning was applied to improve the performance of the model by fine-tuning the pre-trained model given the limited size of the Isarn corpus. The model trained through the transfer learning approach outperformed the model trained from the Isarn dataset alone. © 2022 IEEE.

关键词： Long short-term memory

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MULTI-CROSSRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction 24

MULTI-CROSSRE A Multi-Lingual Multi-Domain Dataset for Relat...

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24th Nordic Conference on Computational Linguistics, NoDaLiDa 2023

作者： Bassignana, Elisa Ginter, Filip Pyysalo, Sampo van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark TurkuNLP Department of Computing University of Turku Finland MaiNLP Center for Information and Language Processing LMU Munich Germany

ISBN: (纸本)9789916219997

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose MULTI-CROSSRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. MULTI-CROSSRE is a machine translated version of CrossRE (Bassignana and Plank, 2022a), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and—as sanity check—over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset. © 2023 Association for Computational Linguistics.

关键词： Data assimilation

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GENERATING SEQUENCES BY LEARNING TO [SELF-]CORRECT 11

GENERATING SEQUENCES BY LEARNING TO [SELF-]CORRECT

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11th International Conference on Learning Representations, ICLR 2023

作者： Welleck, Sean Lu, Ximing West, Peter Brahman, Faeze Shen, Tianxiao Khashabi, Daniel Choi, Yejin Allen Institute for Artificial Intelligence Center for Language and Speech Processing Johns Hopkins University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States

Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present SELF-CORRECTION, an approach that decouples an imperfect base generator (an off-the-shelf language model or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that SELF-CORRECTION improves upon the base generator in three diverse generation tasks-mathematical program synthesis, lexically-constrained generation, and toxicity control- even when the corrector is much smaller than the base generator. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Iterative methods

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CSDNet: cross-sketch with dual gated attention for fine-grained image captioning network

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Multimedia Tools and Applications 2024年 1-28页

作者： Hossain, Md. Shamim Aktar, Shamima Hossen, Md. Bipul Hossain, Mohammad Alamgir Gu, Naijie Huang, Zhangjin School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China Deqing Alpha Innovation Institute Huzhou313299 China Department of Mathematics Jashore University of Science and Technology Jashore7408 Bangladesh Department of Statistics Begum Rokeya University Rangpur5404 Bangladesh National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Anhui Hefei230027 China

In the realm of extracting inter and intra-modal interactions, contemporary models often face challenges such as reduced computational efficiency, particularly when dealing with lengthy visual sequences. To address these issues, this study introduces an innovative model, the Cross-Sketch with Dual Gated Attention Network (CSDNet), designed to handle second-order intra- and inter-modal interactions by integrating a couple of attention modules. Leveraging bilinear pooling to effectively capture these second-order interactions typically requires substantial computational resources due to the processing of large-dimensional tensors. Due to these resource demands, the first module Cross-Sketch Attention (CSA) is proposed, which employs Cross-Tensor Sketch Pooling on attention features to reduce dimensionality while preserving crucial information without sacrificing caption quality. Furthermore, to enhance caption by integrating another novel attention module, Dual Gated Attention (DGA), which contributes additional spatial and channel-wise attention distributions to improve caption generation performance. Our method demonstrates significant computational efficiency improvements, reducing computation time per epoch by an average of 13.54% compared to the base model, which leads to expedited convergence and improved performance metrics. Additionally, we observe a 0.07% enhancement in the METEOR score compared to the base model. Through the application of reinforcement learning optimization, our model achieves a remarkable CIDEr-D score of 132.2% on the MS-COCO dataset. This consistently outperforms baseline performance across a comprehensive range of evaluation metrics. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Tensors

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