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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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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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Towards High-Quality and Efficient speech Bandwidth Extension With Parallel Amplitude and Phase Prediction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, speech and language processing 2024年 33卷 236-250页

作者： Ye-Xin Lu Yang Ai Hui-Peng Du Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China

speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller. This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation. The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs). It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the source narrowband amplitude and phase spectra. To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively. Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods.

关键词： Wideband Narrowband speech enhancement speech processing Hidden Markov models Real-time systems speech coding Predictive models Generators Statistical analysis

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To Know or Not To Know? Analyzing Self-Consistency of Large language Models under Ambiguity

To Know or Not To Know? Analyzing Self-Consistency of Large ...

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2024 Conference on Empirical Methods in Natural language processing, EMNLP 2024

作者： Sedova, Anastasiia Litschko, Robert Frassinelli, Diego Roth, Benjamin Plank, Barbara Faculty of Computer Science Austria UniVie Doctoral School Computer Science Austria Faculty of Philological and Cultural Studies University of Vienna Austria MaiNLP Center for Information and Language Processing LMU Munich Germany Germany

ISBN: (纸本)9798891761681

One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many LLMs suffer from self-inconsistency, which raises doubts about their trustworthiness and reliability. This paper focuses on entity type ambiguity, analyzing the proficiency and consistency of state-of-the-art LLMs in applying factual knowledge when prompted with ambiguous entities. To do so, we propose an evaluation protocol that disentangles knowing from applying knowledge, and test state-of-the-art LLMs on 49 ambiguous entities. Our experiments reveal that LLMs struggle with choosing the correct entity reading, achieving an average accuracy of only 85%, and as low as 75% with underspecified prompts. The results also reveal systematic discrepancies in LLM behavior, showing that while the models may possess knowledge, they struggle to apply it consistently, exhibit biases toward preferred readings, and display self-inconsistencies. This highlights the need to address entity ambiguity in the future for more trustworthy LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Conference on Empirical Methods in Natural language processing, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Arduino Voice Control for Arabic speech Recognition using Smartphone 6

Arduino Voice Control for Arabic Speech Recognition using Sm...

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6th International Hybrid Conference on Informatics and Applied Mathematics, IAM 2023

作者： Bakri, Adil Lounnas, Khaled Lichouri, Mohamed Scientific and Technical Research Centre on Arid Regions CRSTRA Biskra Algeria Scientific Research and Technical Center for the Development of Arabic Language CRSTDLA Algiers Algeria Speech Communication and Signal Processing Laboratory LCPTS Faculty of Electronics and Computer Science USTHB Algiers Algeria

Engaging with our surroundings through voice control has emerged as an increasingly intriguing aspect. This technology is gaining prevalence in our daily lives, whether applied in smart homes, mobile phones, or the control of comfort features in vehicles. The pertinent inquiry is whether voice control will find its place in the production industry, as its reliability remains a critical concern. This project showcases an alternative approach to voice processing by integrating Arabic speech recognition in MIT App Inventor for voice command control. In this setup, voice signals undergo processing, enabling the operation of a LED light circuit through Bluetooth technology. The voice command is given in Arabic through the smartphone device having Bluetooth and the command is transferred and converted to string by the BT Voice Control for Arduino and is transferred to the Bluetooth Module connected to the Arduino board for the control of the LED light circuit. The voice command Arabic is given through a Smartphone device. © 2023 Copyright for this paper by its authors.

关键词： Smartphones

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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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Voice Attribute Editing With Text Prompt

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, speech and language processing 2025年 33卷 1641-1652页

作者： Zheng-Yan Sheng Li-Juan Liu Yang Ai Jia Pan Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

Despite recent advancements in speech generation with text prompt providing control over speech style, voice attributes in synthesized speech remain elusive and challenging to control. This paper introduces a novel task: voice attribute editing with text prompt, with the goal of making relative modifications to voice attributes according to the actions described in the text prompt. To solve this task, VoxEditor, an end-to-end generative model, is proposed. In VoxEditor, addressing the insufficiency of text prompt, a residual memory (ResMem) module is designed, that efficiently maps voice attributes and these descriptors into the shared feature space. Additionally, the ResMem module is enhanced with a voice attribute degree prediction (VADP) module to align voice attributes with corresponding descriptors, addressing the imprecision of text prompt caused by non-quantitative descriptions of voice attributes. We also establish the open-source VCTK-RVA dataset, which leads the way in manual annotations detailing voice characteristic differences among different speakers. Extensive experiments demonstrate the effectiveness and generalizability of our proposed method in terms of both objective and subjective metrics.

关键词： Annotations Training speech Magnetic domains Manuals Magnetic resonance imaging Linguistics Generative adversarial networks Face recognition Electronic mail

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Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study

arXiv

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arXiv 2024年

作者： Chang, Kalvin Robinson, Nathaniel R. Cai, Anna Chen, Ting Zhang, Annie Mortensen, David R. School of Computer Science Carnegie Mellon University United States Center for Language and Speech Processing Johns Hopkins University United States

We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes. We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it comparably effective to sound laws from expert annotation. Our code is publicly available.1 © 2024, CC BY.

关键词： Linguistics

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information processing 2024.

关键词： Sentiment analysis

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