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arXiv 2023年

作者： Litschko, Robert Müller-Eberstein, Max van der Goot, Rob Weber, Leon Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Department of Computer Science IT University of Copenhagen Denmark Munich Germany

language understanding is a multi-faceted cognitive capability, which the Natural language processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large language models (LLMs) the community has witnessed a dramatic shift towards general purpose, task-agnostic approaches powered by generative models. As a consequence, the traditional compartmentalized notion of language tasks is breaking down, followed by an increasing challenge for evaluation and analysis. At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center. Towards this goal, we review existing compartmentalized approaches for understanding the origins of a model’s functional capacity, and provide recommendations for more multifaceted evaluation protocols. © 2023, CC BY.

关键词： Natural language processing systems

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Wav2Nas: An Exploratory Approach to Nasalance Estimation in speech

Wav2Nas: An Exploratory Approach to Nasalance Estimation in ...

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International Symposium on Chinese Spoken language processing

作者： Rui Feng Yu-Ang Chen Yin-Long Liu Jia-Hong Yuan Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Interdisciplinary Research Center for Linguistic Sciences University of Science and Technology of China Hefei

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Nasalance, defined as the ratio of nasal energy to total acoustic energy during speech, is an important metric in speech science and clinical phonetics. Measurement of nasalance, how-ever, requires specialized equipment, which has severely limited its widespread applications. In this study, we explored methods of predicting nasalance from speech waveforms. We designed an oral-nasal separation mask with thermal flow sensors to record the airflows from the mouth and nose separately during speech production, alongside a microphone recording speech sounds. Nasalance was calculated from the oral and nasal airflows, and multilayer perceptron (MLP) models were trained to predict nasalance from speech waveforms. We compared Mel-spectrogram, Mel Frequency Cepstral Coefficients (MFCC), and Wav2vec 2.0 features as inputs to MLPs. The results demonstrated that the Wav2vec 2.0-based features have the highest Pearson Product Moment Correlation Coefficient (PPMC) of 0.7459, outperforming both the Mel-spectrogram and MFCC baselines. These findings emphasize the potential of leveraging pre-trained deep learning models such as Wav2vec 2.0 to predict nasalance directly from raw audio data, reducing reliance on expensive instruments and improving diagnostic capabilities in speech pathology. Moreover, this paper under-scores the promise of deep learning methods in advancing clinical assessment and opens up new avenues for applying compu-tational techniques to better understand and treat speech disorders.

关键词： Deep learning Pathology Nose Production Thermal sensors Predictive models Phonetics Recording Mel frequency cepstral coefficient speech processing

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Keyword-based Natural language Premise Selection for an Automatic Mathematical Statement Proving 16

Keyword-based Natural Language Premise Selection for an Auto...

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16th Workshop on Graph-Based Methods for Natural language processing, TextGraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Dastgheib, Doratossadat Asgari, Ehsaneddin Language Processing and Digital Humanities Lab Tehran Iran Department of Computer and Data Science Shahid Beheshti University Tehran Iran NLP Expert Center Data:Lab Volkswagen AG Munich Germany

Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Extraction

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Wiktionary Normalization of Translations and Morphological Information 28

Wiktionary Normalization of Translations and Morphological I...

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28th International Conference on Computational Linguistics, COLING 2020

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

ISBN: (纸本)9781952148279

We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation—clipping, contraction, and eye dialect—and improve upon a standard attention baseline by using copy attention. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Translation (languages)

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Transfer learning for automated responses to the BDI questionnaire

Transfer learning for automated responses to the BDI questio...

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2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

作者： Spartalis, Christoforos Drosatos, George Arampatzis, Avi Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

This paper describes the participation of the DUTH-ATHENA team of Democritus University of Thrace and Athena Research center in the eRisk 2021 task, which focuses on measuring the level of depression based on Reddit users' posts. We address this task using both feature-based and fine-tuning strategies for applying BERT-based representations. In the feature-based approaches, we examine the possibilities of a SBERT model based on RoBERTa, pre-trained on Natural language Inference (NLI) data and fine-tuned on STSb dataset to leverage transfer learning to depression-level estimation, and we achieve promising results. One of our runs ranks first in Average Hit Rate (AHR), while the others rank among the best four in the other evaluation metrics. Also, for the fine-tuning approach, we propose two predictive models that are built upon RoBERTa, which provide directions for future optimizations. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social networking (online)

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

arXiv

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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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Entity Linking in the Job Market Domain

arXiv

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arXiv 2024年

作者： Zhang, Mike van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark Pioneer Centre for Artificial Intelligence Copenhagen Denmark MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany

In Natural language processing, entity linking (EL) has centered around Wikipedia, but remains underexplored for the job market domain. Disambiguating skill mentions can help us to get insight into the labor market demands. In this work, we are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014). Previous efforts linked coarse-grained (full) sentences to a corresponding ESCO skill. In this work, we link more fine-grained span-level mentions of skills. We tune two high-performing neural EL models, a bi-encoder (Wu et al., 2020) and an autoregressive model (Cao et al., 2021), on a synthetically generated mention–skill pair dataset and evaluate them on a human-annotated skill-linking benchmark. Our findings reveal that both models are capable of linking implicit mentions of skills to their correct taxonomy counterparts. Empirically, BLINK outperforms GENRE in strict evaluation, but GENRE performs better in loose evaluation (accuracy@k). Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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PQLM - Multilingual Decentralized Portable Quantum language Model

PQLM - Multilingual Decentralized Portable Quantum Language ...

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

作者： Shuyue Stella Li Xiangyu Zhang Shu Zhou Hongchao Shu Ruixing Liang Hexin Liu Leibny Paola Garcia Center for Language and Speech Processing Johns Hopkins University Department of Physics Hong Kong University of Science and Technology School of Electrical and Electronic Engineering Nanyang Technological University Human Language Technology Center of Excellence Johns Hopkins University

With careful manipulation, malicious agents can reverse engineer private information encoded in pre-trained language models. Security concerns motivate the development of quantum pre-training. In this work, we propose a highly portable quantum language model (PQLM) that can easily transmit information to downstream tasks on classical machines. The framework consists of a cloud PQLM built with random Variational Quantum Classifiers (VQC) and local models for downstream applications. We demonstrate the ad hoc portability of the quantum model by extracting only the word embeddings and effectively applying them to downstream tasks on classical machines. Our PQLM exhibits comparable performance to its classical counterpart on both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy) metrics. We also perform ablation studies on the factors affecting PQLM performance to analyze model stability. Our work establishes a theoretical foundation for a portable quantum pre-trained language model that could be trained on private data and made available for public use with privacy protection guarantees.

关键词： Training Sentiment analysis Social networking (online) Computational modeling Signal processing Stability analysis Servers

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

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

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International computer science and Engineering Conference (ICSEC)

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

ISBN: (纸本)9781665462730

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.

关键词： Training computer science Recurrent neural networks Protocols Computational modeling Transfer learning Bidirectional control

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SL-REDU GSL: A Large Greek Sign language Recognition Corpus

SL-REDU GSL: A Large Greek Sign Language Recognition Corpus

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Acoustics, speech, and Signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Katerina Papadimitriou Galini Sapountzaki Kyriaki Vasilaki Eleni Efthimiou Stavroula-Evita Fotinea Gerasimos Potamianos Department of Electrical & Computer Engineering University of Thessaly Volos Greece Department of Special Education University of Thessaly Volos Greece Institute for Language & Speech Processing Athena Research & Innovation Center Athens Greece

We present a large multi-signer video corpus for the Greek Sign language (GSL), suitable for the development and evaluation of GSL recognition algorithms. The database has been collected as part of the “SL-ReDu” project that focuses on the education use-case of systematic teaching of GSL as a second language (L2). The project aims to assist this process by allowing self-monitoring and objective assessment of GSL learners’ productions through the use of recognition technology, thus requiring suitable data resources relevant to the aforementioned use-case. To this end, we present the SL-ReDu GSL corpus, an extensive RGB+D video collection of 21 informants with a duration of 36 hours, recorded under studio conditions, consisting of: (i) isolated signs; (ii) continuous signing (annotated at the sentence level); and (iii) fingerspelling of words. We provide a detailed description of the design and acquisition methods used to develop it, along with corpus statistics and a comparison to existing sign language datasets. The SL-ReDu GSL corpus, as well as proposed frameworks for recognition experiments on it, are publicly available at https://***/corpus.

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