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END-TO-END LYRICS RECOGNITION WITH SELF-SUPERVISED LEARNING

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arXiv 2022年

作者： Zhang, Xiangyu Li, Shuyue Stella He, Zhanhong Togneri, Roberto Garcia, Leibny Paola Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Department of Computer Science University of Western Australia Australia

Lyrics recognition is an important task in music processing. Despite traditional algorithms such as the hybrid HMM-TDNN model achieving good performance, studies on applying end-to-end models and self-supervised learning (SSL) are limited. In this paper, we first establish an end-to-end baseline for lyrics recognition and then explore the performance of SSL models on lyrics recognition task. We evaluate a variety of upstream SSL models with different training methods (masked reconstruction, masked prediction, autoregressive reconstruction, and contrastive learning). Our end-to-end self-supervised models, evaluated on the DAMP music dataset, outperform the previous state-of-the-art (SOTA) system by 5.23% for the dev set and 2.4% for the test set even without a language model trained by a large corpus. Moreover, we investigate the effect of background music on the performance of self-supervised learning models and conclude that the SSL models cannot extract features efficiently in the presence of background music. Finally, we study the out-of-domain generalization ability of the SSL features considering that those models were not trained on music datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

Silver Syntax Pre-training for Cross-Domain Relation Extraction

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arXiv 2023年

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

Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size of current RE datasets: obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically be repeated for each new domain. An intermediate training step on data from related tasks has shown to be beneficial across many NLP tasks. However, this setup still requires supplementary annotated data, which is often not available. In this paper, we investigate intermediate pre-training specifically for RE. We exploit the affinity between syntactic structure and semantic RE, and identify the syntactic relations which are closely related to RE by being on the shortest dependency path between two entities. We then take advantage of the high accuracy of current syntactic parsers in order to automatically obtain large amounts of low-cost pre-training data. By pre-training our RE model on the relevant syntactic relations, we are able to outperform the baseline in five out of six cross-domain setups, without any additional annotated data. © 2023, CC BY.

关键词： Syntactics

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

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arXiv 2023年

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, CC BY.

关键词： Extraction

Addressing the Challenges of Cross-Lingual Hate speech Detection

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arXiv 2022年

作者： Bigoulaeva, Irina Hangya, Viktor Gurevych, Iryna Fraser, Alexander Department of Computer Science Technical University of Darmstadt Center for Information and Language Processing LMU Munich Germany

The goal of hate speech detection is to filter negative online content aiming at certain groups of people. Due to the easy accessibility of social media platforms it is crucial to protect everyone which requires building hate speech detection systems for a wide range of languages. However, the available labeled hate speech datasets are limited making it problematic to build systems for many languages. In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language, which lacks labeled examples, and show that good performance can be achieved. We then incorporate unlabeled target language data for further model improvements by bootstrapping labels using an ensemble of different model architectures. Furthermore, we investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance. We test simple data undersampling and oversampling techniques and show their effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： speech

A Novel Method for Audiogram Digitization in Audiological Reports

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IEEE Access 2024年 12卷 37862-37872页

作者： Yang, Ta-Wei Yang, Chen-Yu Lee, Yen-Hui Chen, Pey-Yu Lin, Pei-Hsuan Wu, Wei-Cheng Wu, Chen-Chi Chou, Cheng-Fu Graduate Institute of Networking and Multimedia National Taiwan University Taipei106319 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei106319 Taiwan National Taiwan University Hospital Hsin-Chu Branch Department of Otolaryngology Hsinchu302058 Taiwan National Taiwan University Hospital Hearing and Speech Center Taipei100225 Taiwan National Taiwan University Hospital Department of Otolaryngology Taipei100225 Taiwan MacKay Memorial Hospital Department of Otolaryngology Taipei104217 Taiwan Mackay Medical College Department of Audiology and Speech-Language Pathology New Taipei City252005 Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei100025 Taiwan National Taiwan University Hospital Department of Medical Genetics Taipei100229 Taiwan National Taiwan University Hospital Hsin-Chu Branch Department of Medical Research Hsinchu302058 Taiwan

An audiogram records the hearing status, including each hearing threshold at multiple frequencies. While deep learning is gradually maturing in the clinical research approach, audiologists could speed up their diagnostic process with audiogram digitization for handwritten graphs or electronically generated images from instruments. However, given the diversity of audiogram symbols and formats, the existing audiogram digitization model has room for improvement in recognition accuracy. We propose a multi-stage workflow to enhance accuracy by integrating YOLOv5 and the optical character recognition (OCR) model. Our proposed audiogram digitization model could identify all audiogram symbols with an accuracy rate of 98%. We hope that this model could help future research in audiology. © 2013 IEEE.

关键词： Data mining

HFabD+M: A Web-based Platform for Automated Hyperledger Fabric Deployment and Management

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HFabD+M: A Web-based Platform for Automated Hyperledger Fabr...

Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Ioannis Zikos Andreas Sendros George Drosatos Pavlos S. Efraimidis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute for Language and Speech Processing Athena Research Center Xanthi Greece

Hyperledger Fabric is an open-source private permissioned blockchain that supports the use of smart contracts (chaincode). It is aimed mainly at private networks of companies. To serve the different needs of each company and to be flexible in customer requirements, it consists of various adaptive components. Although this structure efficiently addresses a wide range of needs, deploying such a network for research purposes or rapid development is complex. In this paper, we present a web-based system architecture for the automated deployment of a Hyperledger Fabric network, and in addition, we describe the tools needed to manage and update such a network. Finally, as a proof-of-concept, we implement the proposed architecture to demonstrate the feasibility of our approach.

关键词： Distributed ledger Smart contracts Prototypes Systems architecture Companies Fabrics Blockchains

Blockchain-Based Framework for Stock Market Operations: IPOs, Trading, and Dividend Distribution

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Blockchain-Based Framework for Stock Market Operations: IPOs...

IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Muhammad Sharshar Kareem Elzeky Menna Othman SeifAldin Khaled Hosam Moubarak Walid Gomaa Department of Computer Science Center for Secure Cyber-Physical Systems (C2PS) Khalifa University UAE Department of Natural Language Processing MBZUAI UAE Department of Computer Engineering Cyber-Physical Lab Egypt-Japan University of Science and Technology Egypt Accounting Department Egypt-Japan University of Science and Technology Egypt

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

This study examines blockchain technology's potential to enhance the stock market by offering a decentralized alternative to the traditional system. The study explores the challenges of the current traditional stock market, such as long settlement times, weak transparency, dependency on centralized clearing houses, and heightened fees. The proposed novel system leverages blockchain technology to create a fully decentralized stock market encompassing all fundamental operations from Initial Public Offerings (IPOs) to stock trading and extending to distributing profits or dividends. It connects all market participants through an Ethereum consortium blockchain fuelled by smart contracts. This approach replaces traditional intermediaries like brokers with automated regulations, ensuring more rapid transaction settlements, reduced operational errors and fees, and strengthened security measures. The proposed blockchain-based system, consisting of four smart contracts for IPO, stock exchange, dividend distribution, and participant management, demonstrates remarkable performance, showing promise in efficiently handling the current load of the largest stock markets like the London Stock Exchange (LSE). Through theoretical and practical testing, the system achieved a remarkable throughput of 120 transactions per second and latency or finality of just 4 seconds, underscoring its capability to significantly enhance the efficiency of stock market operations. 1 1 The implementation code is available via GitHub upon request.

关键词： Cloud computing Smart contracts Transforms Throughput Regulation Blockchains Security Stock markets Testing Software development management

EDSep: An Effective Diffusion-Based Method for speech Source Separation

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EDSep: An Effective Diffusion-Based Method for Speech Source...

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

作者： Jinwei Dong Xinsheng Wang Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy.

关键词： Training Adaptation models Technological innovation Source separation Databases Stochastic processes Differential equations speech enhancement Diffusion models Mathematical models

KnowLogic: A Benchmark for Commonsense Reasoning via Knowledge-Driven Data Synthesis

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arXiv 2025年

作者： Zhan, Weidong Wang, Yue Hu, Nan Xiao, Liming Ma, Jingyuan Qin, Yuhang Li, Zheng Yang, Yixin Deng, Sirui Ding, Jinkun Ma, Wenhan Li, Rui Luo, Weilin Liu, Qun Sui, Zhifang Center for Chinese Linguistics Department of Chinese Language and Literature Peking University China School of Computer Science State Key Laboratory of Multimedia Information Processing Peking University China Huawei Noah’s Ark Lab China

Current evaluations of commonsense reasoning in LLMs are hindered by the scarcity of natural language corpora with structured annotations for reasoning tasks. To address this, we introduce KnowLogic, a benchmark generated through a knowledge-driven synthetic data strategy. KnowLogic integrates diverse commonsense knowledge, plausible scenarios, and various types of logical reasoning. One of the key advantages of KnowLogic is its adjustable difficulty levels, allowing for flexible control over question complexity. It also includes fine-grained labels for in-depth evaluation of LLMs’ reasoning abilities across multiple dimensions. Our benchmark consists of 3,000 bilingual (Chinese and English) questions across various domains, and presents significant challenges for current LLMs, with the highest-performing model achieving only 69.57%. Our analysis highlights common errors, such as misunderstandings of low-frequency commonsense, logical inconsistencies, and overthinking. This approach, along with our benchmark, provides a valuable tool for assessing and enhancing LLMs’ commonsense reasoning capabilities and can be applied to a wide range of knowledge domains. Our data and code can be found at https://***/pokerwf/ KnowLogic. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems