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22nd Workshop on Biomedical Natural language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Šuster, Simon Baldwin, Timothy Verspoor, Karin School of Computing and Information Systems The University of Melbourne Australia School of Computing Technologies RMIT University Australia Department of Natural Language Processing MBZUAI United Arab Emirates

ISBN: (纸本)9781959429852

Automatically rating the quality of published research is a critical step in medical evidence synthesis. While several methods have been proposed, their algorithmic fairness has been overlooked, even though significant risks may result when such systems are deployed in biomedical contexts. In this work, we study the fairness of two systems with respect to two sensitive attributes: participant sex and medical area. In some cases, we find important inequalities, leading us to apply various debiasing methods. Upon examining the interplay of predictive performance and fairness, as well as medically-critical selective classification capabilities and calibration performance, we find that it is possible to improve fairness through debiasing, but often at a cost to other performance measures. © 2023 Association for Computational Linguistics.

关键词： Classification (of information)

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria Engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization 29

LipKey: A Large-Scale News Dataset for Absent Keyphrases Gen...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Koto, Fajri Baldwin, Timothy Lau, Jey Han School of Computing and Information Systems The University of Melbourne Australia Department of Natural Language Processing MBZUAI United Arab Emirates

Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Large dataset

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Unsupervised Lexical Substitution with Decontextualised Embeddings 29

Unsupervised Lexical Substitution with Decontextualised Embe...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wada, Takashi Baldwin, Timothy Matsumoto, Yuji Lau, Jey Han School of Computing and Information Systems The University of Melbourne Australia Japan Department of Natural Language Processing MBZUAI United Arab Emirates

We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of substitute candidates, reducing morphophonetic or morphosyntactic biases induced by article–noun agreement. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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On the Need to Use Smart Contracts in Enterprise Application Integration 25

On the Need to Use Smart Contracts in Enterprise Application...

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25th Ibero-American Conference on Software Engineering, CIbSE 2022

作者： Parahyba, Fernando Dornelles, Eldair F. Roos-Frantz, Fabricia Frantz, Rafael Z. Molina-Jiménez, Carlos Reina Quintero, Antonia M. Bocanegra, José Sawicki, Sandro Unijuí University Brazil Department of Computer Science and Technology University of Cambridge United Kingdom Department of Language and Systems University of Seville Spain Department of Systems and Computing Engineering Universidad de los Andes Colombia

ISBN: (纸本)9781713855583

Integration processes involve Business Constraints and Service Level Agreements that, with current technology, are not monitored or enforced automatically at run–time. This approach leaves the participants with no means of supervising the development of their interactions or of collecting indisputable evidence to ease the resolution of disputes that can potentially emerge. In this paper, to address the issue, we suggest the inclusion of smart contracts in integration processes to supervise and mediate, at run–time, the agreements to which the participants commit. We discuss the requirements that smart contracts for integration processes need to meet and the challenges involved in writing, executing, deploying, and verifying them. © 2022 CIbSE 2022 - XXV Ibero-American Conference on Software Engineering. All rights reserved.

关键词： Smart contract

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Advances in a DSL to Specify Smart Contracts for Application Integration Processes 25

Advances in a DSL to Specify Smart Contracts for Application...

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25th Ibero-American Conference on Software Engineering, CIbSE 2022

作者： Dornelles, Eldair F. Parahyba, Fernando Frantz, Rafael Z. Roos-Frantz, Fabricia Reina Quintero, Antonia M. Molina-Jiménez, Carlos Bocanegra, José Sawicki, Sandro Unijuí University Brazil Department of Language and Systems University of Seville Spain Department of Computer Science and Technology University of Cambridge United Kingdom Department of Systems and Computing Engineering Universidad de los Andes Colombia

ISBN: (纸本)9781713855583

Several languages have been developed for writing smart contracts for specific domains, such as health, finance, and business processes. However, none of them includes the constructors needed for writing smart contracts used in application integration processes which have their own particularities. Such smart contracts are responsible for monitoring the communication between the process and the applications to guarantee that the rights of the parties involved in the integration process are observed. To cover the gap, this paper discusses a domain-specific language for writing smart contracts in the application integration domain. It provides constructors to write smart contracts with a level of abstraction close to the natural language used in conventional contracts. © 2022 CIbSE 2022 - XXV Ibero-American Conference on Software Engineering. All rights reserved.

关键词： Smart contract

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Does Representational Fairness Imply Empirical Fairness? 2

Does Representational Fairness Imply Empirical Fairness?

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2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural language Processing, AACL-IJCNLP 2022

作者： Shen, Aili Han, Xudong Cohn, Trevor Baldwin, Timothy Frermann, Lea Amazon Alexa Ai Australia School of Computing and Information Systems The University of Melbourne Australia Department of Natural Language Processing Mbzuai United Arab Emirates

ISBN: (纸本)9781959429043

NLP technologies can cause unintended harmsif learned representations encode sensitive attributes of the author, or predictions systematically vary in quality across groups. Populardebiasing approaches, like adversarial training,remove sensitive information from representations in order to reduce disparate performance,however the relation between representationalfairness and empirical (performance) fairnesshas not been systematically studied. This paperfills this gap, and proposes a novel debiasingmethod building on contrastive learning to encourage a latent space that separates instancesbased on target label, while mixing instancesthat share protected attributes. Our results showthe effectiveness of our new method and, moreimportantly, show across a set of diverse debiasing methods that representational fairnessdoes not imply empirical fairness. This workhighlights the importance of aligning and understanding the relation of the optimization objective and final fairness target. Our code is available at: https://***/AiliAili/contrastive learningrepo. © AACL-IJCNLP *** rights reserved

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Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

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Computers, Materials & Continua 2023年第3期74卷 5447-5465页

作者： Badriyya B.Al-onazi Saud S.Alotaib Saeed Masoud Alshahrani Najm Alotaibi Mrim M.Alnfiai Ahmed S.Salama Manar Ahmed Hamza Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural language Processing(NLP)*** Arabic language has a lot of *** instance,it is the fourth mostly-used language on the internet and the sixth official language of ***,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic *** general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the *** this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is *** major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic *** first step in the proposed model is to pre-process the input data to transform it into a useful *** Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic *** the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s *** proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

关键词： Hybrid deep learning natural language processing arabic language text classification parameter tuning

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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Computer systems Science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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Location Aware Modular Biencoder for Tourism Question Answering

arXiv

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arXiv 2024年

作者： Li, Haonan Tomko, Martin Baldwin, Timothy School of Computing and Information Systems The University of Melbourne Australia Department of Infrastructure Engineering The University of Melbourne Australia Department of Natural Language Processing MBZUAI United Arab Emirates

Answering real-world tourism questions that seek Point-of-Interest (POI) recommendations is challenging, as it requires both spatial and non-spatial reasoning, over a large candidate pool. The traditional method of encoding each pair of question and POI becomes inefficient when the number of candidates increases, making it infeasible for real-world applications. To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. We use pretrained language models (PLMs) to encode textual information, and train a location encoder to capture spatial information of POIs. Experiments on a real-world tourism QA dataset demonstrate that our approach is effective, efficient, and outperforms previous methods across all metrics. Enabled by the dense retrieval architecture, we further build a global evaluation baseline, expanding the search space by 20 times compared to previous work. We also explore several factors that impact on the model’s performance through follow-up experiments. Our code and model are publicly available at https://***/haonan-li/LAMB. © 2024, CC BY.

关键词： Vector spaces

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