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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Deng, Xun Hu, Lun You, Zhu-Hong Hu, Peng-Wei Chinese Academy of Sciences The Xinjiang Technical Institute of Physics and Chemistry Urumqi830011 China University of Chinese Academy of Sciences Beijing100049 China Xinjiang Laboratory of Minority Speech and Language Information Processing Urumqi830011 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China

ISBN: (纸本)9798350337488

In medical research, organoids, which exhibit structural and functional resemblances to authentic organs, offer a substantial avenue for delving into aspects encompassing physiology, pathophysiology, diseases, and pharmaceutical screening. Critical insights into drug responsiveness are often gleaned from these organoids' dimensional and configurational disparities. However, conventional detection methodologies reliant upon fluorescent labeling engender potential hazards to organoid integrity, thereby impinging upon their intrinsic dynamic attributes. Traditional bounding-box detection methodologies fall short in encapsulating intricate morphological particulars, and certain deep-learning approaches grapple with the intricate task of capturing multi-scale data, particularly when tasked with discerning organoid structures characterized by marked shape and size heterogeneities. In a bid to surmount these constraints, our study introduces CAMPEOD, an innovative framework that synergistically amalgamates multi-scale attributes derived from organoid specimens, employing cross-attention mechanisms. This novel approach effectively obviates superfluous background interference and image noise, thereby endowing an automated, finely-tuned dissection of organoid samples. Significantly, this segmentation process ensures congruence with authentic organoid quantities and morphological characteristics. By facilitating comprehensive scrutiny of microscopy images of organoid samples on a large scale, CAMPEOD assumes considerable implications for the realm of pharmaceutical screening and ailment emulation. © 2023 IEEE.

关键词： automated cross-attention multi-scale organoids segmentation

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Constraining Sequential Model Editing with Editing Anchor Compression

arXiv

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arXiv 2025年

作者： Xu, Hao-Xiang Ma, Jun-Yu Ling, Zhen-Hua Zhang, Ningyu Gu, Jia-Chen National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China Zhejiang University China University of California Los Angeles United States

Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining these models, there is an increasing focus on developing model editing. However, the general abilities of LLMs across downstream tasks are prone to significant degradation during sequential editing. This paper statistically observes that the parameter matrix after editing exhibits a significant deviation compared to its previous state as the number of edits increases. This serious deviation affects the original knowledge associations within LLMs and leads to the degradation of their general abilities. To this end, a framework termed Editing Anchor Compression (EAC) is proposed to constrain the deviation of the parameter matrix during sequential editing. It compresses the editing information by selecting editing anchors that are important in encoding new relations without deviating too much from the original matrix, thereby preserving the general abilities. Experiments of applying EAC to two popular editing methods on three LLMs across four tasks are conducted. Evaluation results show that EAC effectively minimizes unreasonable deviations caused by model editing, preserving over 70% of the general abilities while better retaining the editing knowledge compared to the original counterpart methods1 Copyright © 2025, The Authors. All rights reserved.

关键词： Matrix algebra

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U2-KWS: Unified Two-Pass Open-Vocabulary Keyword Spotting with Keyword Bias

U2-KWS: Unified Two-Pass Open-Vocabulary Keyword Spotting wi...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Ao Zhang Pan Zhou Kaixun Huang Yong Zou Ming Liu Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xian China Space AI Li Auto

Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has attracted increasingly more interest. However, existing methods based on acoustic models and post-processing train the acoustic model with ASR training criteria to model all phonemes, making the acoustic model under-optimized for the KWS task. To solve this problem, we propose a novel unified two-pass open-vocabulary KWS (U2-KWS) framework inspired by the two-pass ASR model U2. Specifically, we employ the CTC branch as the first stage model to detect potential keyword candidates and the decoder branch as the second stage model to validate candidates. In order to enhance any customized keywords, we redesign the U2 training procedure for U2-KWS and add keyword information by audio and text cross-attention into both branches. We perform experiments on our internal dataset and Aishell-1. The results show that U2-KWS can achieve a significant relative wake-up rate improvement of 41 % compared to the traditional customized KWS systems when the false alarm rate is fixed to 0.5 times per hour.

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Algorithmic Diversity and Tiny Models: Comparing Binary Networks and the Fruit Fly Algorithm on Document Representation Tasks 3

Algorithmic Diversity and Tiny Models: Comparing Binary Netw...

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3rd Workshop on Simple and Efficient Natural language processing, SustaiNLP 2022

作者： Ceron, Tanise Truong, Nhut Herbelot, Aurelie Institute for Natural Language Processing University of Stuttgart Germany Center for Mind/Brain Sciences University of Trento Italy Department of Information Engineering and Computer Science University of Trento Italy

ISBN: (纸本)9781959429241

Neural language models have seen a dramatic increase in size in the last years. While many still advocate that 'bigger is better', work in model distillation has shown that the number of parameters used by very large networks is actually more than what is required for state-of-the-art performance. This prompts an obvious question: can we build smaller models from scratch, rather than going through the inefficient process of training at scale and subsequently reducing model size? In this paper, we investigate the behaviour of a biologically inspired algorithm, based on the fruit fly's olfactory system. This algorithm has shown good performance in the past on the task of learning word embeddings. We now put it to the test on the task of semantic hashing. Specifically, we compare the fruit fly to a standard binary network on the task of generating locality-sensitive hashes for text documents, measuring both task performance and energy consumption. Our results indicate that the two algorithms have complementary strengths while showing similar electricity usage. © 2022 Association for Computational Linguistics.

关键词： Distillation

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

arXiv

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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

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speech Reconstruction from Silent Tongue and Lip Articulation by Pseudo Target Generation and Domain Adversarial Training

Speech Reconstruction from Silent Tongue and Lip Articulatio...

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

作者： Rui-Chen Zheng Yang Ai Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei P. R. China

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acoustic conversion, and may also be refered to as a silent speech interface. We propose to employ a method built on pseudo target generation and domain adversarial training with an iterative training strategy to improve the intelligibility and naturalness of the speech recovered from silent tongue and lip articulation. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in silent speaking mode compared to the baseline TaLNet model. When using an automatic speech recognition (ASR) model to measure intelligibility, the word error rate (WER) of our proposed method decreases by over 15% compared to the baseline. In addition, our proposed method also outperforms the baseline on the intelligibility of the speech reconstructed in vocalized articulating mode, reducing the WER by approximately 10%.

关键词： Training Tongue Ultrasonic imaging Lips speech Iterative methods Task analysis

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Pre-training language Model as a Multi-perspective Course Learner

arXiv

引用

arXiv 2023年

作者： Chen, Beiduo Huang, Shaohan Zhang, Zihan Guo, Wu Ling, Zhenhua Huang, Haizhen Wei, Furu Deng, Weiwei Zhang, Qi National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China Microsoft Corporation Beijing China

ELECTRA (Clark et al., 2020), the generator-discriminator pre-training framework, has achieved impressive semantic construction capability among various downstream tasks. Despite the convincing performance, ELECTRA still faces the challenges of monotonous training and deficient interaction. Generator with only masked language modeling (MLM) leads to biased learning and label imbalance for discriminator, decreasing learning efficiency;no explicit feedback loop from discriminator to generator results in the chasm between these two components, underutilizing the course learning. In this study, a multi-perspective course learning (MCL) method is proposed to fetch a many degrees and visual angles for sample-efficient pre-training, and to fully leverage the relationship between generator and discriminator. Concretely, three self-supervision courses are designed to alleviate inherent flaws of MLM and balance the label in a multi-perspective way. Besides, two self-correction courses are proposed to bridge the chasm between the two encoders by creating a "correction notebook" for secondary-supervision. Moreover, a course soups trial is conducted to solve the "tug-of-war" dynamics problem of MCL, evolving a stronger pre-trained model. Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks, and overshadows recent advanced ELECTRA-style models under the same settings. The pre-trained MCL model is available at https://***/McmanusChen/MCL-base. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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

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

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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

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A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

arXiv

引用

arXiv 2023年

作者： Li, Shuyue Stella Xu, Beining Zhang, Xiangyu Liu, Hexin Chao, Wenhan Garcia, Leibny Paola Center for Language and Speech Processing Johns Hopkins University United States School of Computer Science and Engineering Beihang University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature representations. Using automatic speech recognition (ASR) as a downstream task, we analyze the effect of model size, training objectives, and model architecture on the models’ performance as a feature extractor for a set of topologically diverse corpora. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations using deep generalized canonical correlation analysis. Results show the contrastive loss in the wav2vec2.0 objective facilitates more effective cross-lingual feature extraction. There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings. The proposed metric is an effective indicator of the quality of the representations and can be useful for model selection.1 © 2023, CC BY.

关键词： Topology

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