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Computer Science, Artificial Intelligence
Tahani H. Alwaneen, Aqil M. Azmi, Hatim A. Aboalsamh, Erik Cambria, Amir Hussain
Summary: Question answering is a subfield of information retrieval that aims to answer questions posed in natural language. The development of Arabic question answering systems has been hindered by linguistic challenges and limited resources. Research in this area includes examining challenges, existing systems, techniques, and future directions for Arabic question answering systems.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Maxwell A. Weinzierl, Sanda M. Harabagiu
Summary: This study designed a QA system capable of answering ad-hoc questions about COVID-19, using automatic question generation and question entailment. The results showed that the system achieved state-of-the-art performance with expert questions and competitive performance with consumer questions. However, more than half of the answers were missed due to limitations in the relevance models used. Improvements should be considered for better relevance models and enhanced inference methods.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2023)
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Geochemistry & Geophysics
Zhenghang Yuan, Lichao Mou, Zhitong Xiong, Xiao Xiang Zhu
Summary: The detection of changes on the Earth's surface is crucial for urban planning and sustainability. However, current change detection techniques are only accessible to experts. To address this, the study introduces a new task called change detection-based visual question answering (CDVQA) on multitemporal aerial images, enabling users to obtain change-based information easily. The study presents a CDVQA dataset and a baseline framework along with different strategies for improving the performance of the CDVQA task. The results offer valuable insights for future CDVQA research.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Pengpeng Zeng, Haonan Zhang, Lianli Gao, Jingkuan Song, Heng Tao Shen
Summary: This paper addresses the challenges of utilizing prior knowledge and structured visual information in Video Question Answering (VideoQA). The proposed Prior Knowledge and Object-sensitive Learning (PKOL) approach effectively integrates prior knowledge and learns object-sensitive representations to enhance the VideoQA task. The experiments demonstrate consistent improvements and state-of-the-art performance on competitive benchmarks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Asmaa Alrayzah, Fawaz Alsolami, Mostafa Saleh
Summary: Artificial intelligence-based question-answering systems have a significant impact on various tasks, but the complexities of modern Arabic present challenges for Arabic QA systems. Current research on Arabic QA has been limited to small datasets and lacks consideration of recent techniques. This article analyzes existing studies on Arabic QA systems and provides insights into limitations, concerns, and recommendations for future research.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Software Engineering
Hela Fehri, Sondes Dardour, Kais Haddar
Summary: This article introduces ARmed, an automatic question answering system for medical questions in Arabic language. ARmed successfully addresses the conflict between the extracted answer and user's requirements in the medical domain and has the potential to handle a large number of question and answer types.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
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Computer Science, Information Systems
Aihua Mao, Zhi Yang, Ken Lin, Jun Xuan, Yong-Jin Liu
Summary: This paper introduces a novel positional attention guided Transformer-like architecture to address the challenge of utilizing positional information in visual question answering (VQA) tasks. Experimental results demonstrate that the proposed model outperforms state-of-the-art models and performs particularly well in handling object counting questions.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Ivo Pisacovic, Frantisek Darena, David Prochazka, Vit Janis
Summary: Establishing normative documents is essential for larger organizations to control processes and provide solutions to common problems, but the formal and difficult-to-read nature of these documents necessitates different customer services. Companies are increasingly developing chatbots for firstline customer support automation, but automatic answering directly from normative documents is often ineffective. A novel preprocessing method is proposed in this paper to improve the accuracy of automatic question answering on normative documents.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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Computer Science, Artificial Intelligence
Zhenguo Yang, Jiale Xiang, Jiuxiang You, Qing Li, Wenyin Liu
Summary: This paper introduces a new E-VQA dataset that includes free-form questions and answers for real-world event concepts, providing context information of events as domain knowledge. The dataset is valuable for researching and evaluating VQA methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Yao Wang, Chuhan Jiao, Mihai Bace, Andreas Bulling
Summary: This study proposes a question-answering paradigm to investigate the recallability of visualizations and creates a new dataset called VisRecall, which includes visualizations annotated with recallability scores from crowd-sourced human participants. Additionally, the study introduces a computational method for predicting the recallability of different visualization elements and demonstrates its effectiveness on the VisRecall dataset.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Information Systems
Yongqi Li, Nan Yang, Liang Wang, Furu Wei, Wenjie Li
Summary: To address the issue of question ambiguity in conversation question answering, a generative retrieval method called GCoQA is proposed. GCoQA retrieves passages by generating their identifiers token-by-token via an encoder-decoder architecture, leading to significant improvements in passage retrieval and document retrieval compared to current methods.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Jinlu Zhang, Jing He, Yiyi Zhou, Xiaoshuai Sun, Xiao Yu
Summary: In recent years, Question Answering (QA) systems have become popular for knowledge acquisition. However, the prevailing approach of matching question-answer pairs suffers from low precision and efficiency due to the ambiguity of natural language descriptions. To address this, we propose a hierarchical semantic matching-based QA approach, called HSM-QA. It involves query-question matching using a Siamese network for similarity calculation and query-answer matching using a pairwise algorithm and single-stream structure for relevance calculation. Experimental results demonstrate the superior performance and efficiency of our HSM-QA compared to other methods, along with the use of a new dataset generated from Chinese social media.
Article
Computer Science, Interdisciplinary Applications
Thannarot Kunlamai, Tatsuro Yamane, Masanori Suganuma, Pang-Jo Chun, Takayaki Okatani
Summary: This paper explores the application of visual question answering (VQA) in bridge inspection using recent advancements in multimodal artificial intelligence (AI) systems. VQA involves an AI model providing natural language answers to questions about the content of an input image. To address the challenge of creating training data for VQA in bridge inspection, the paper proposes leveraging existing bridge inspection reports, which already include image-text pairs, as external knowledge to enhance VQA performance. The results demonstrate a significant improvement in VQA accuracy using this approach, highlighting the potential of AI models for VQA as valuable tools for assessing the condition of bridges.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
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Computer Science, Information Systems
Mariam M. Biltawi, Sara Tedmori, Arafat Awajan
Summary: Question-answering (QA) systems aim to provide answers for questions by extracting or generating from text. Arabic poses challenges as a language with limited research despite a large number of native speakers. Some QA systems have been developed for Arabic text, which can be further analyzed in different components such as question analysis and information retrieval.
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Computer Science, Interdisciplinary Applications
Fuze Cong, Shibiao Xu, Li Guo, Yinbing Tian
Summary: This paper proposes two medical-specific modules to utilize weakly supervised anomaly localization information and enhances question-reasoning ability and model generalization performance by combining the Transformer decoder and multi-task learning strategy.
IEEE TRANSACTIONS ON MEDICAL IMAGING
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Katia Vila, Antonio Fernandez, Jose M. Gomez, Antonio Ferrandez, Josval Diaz
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Linguistics
Lluis-F Hurtado, Fernando Garcia, Emilio Sanchis, Encarna Segarra
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Proceedings Paper
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Joaquin Planells, Lluis-F. Hurtado, Emilio Sanchis, Encarna Segarra
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F. Garcia, L. F. Hurtado, E. Segarra, E. Sanchis, Giuseppe Riccardi
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Proceedings Paper
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Marcos Calvo, Lluis-F. Hurtado, Fernando Garcia, Emilio Sanchis
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Proceedings Paper
Computer Science, Artificial Intelligence
Joan Pastor, Lluis-F. Hurtado, Encarna Segarra, Emilio Sanchis
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
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Proceedings Paper
Computer Science, Artificial Intelligence
Fernando Garcia, Lluis-F. Hurtado, Emilio Sanchis, Encarna Segarra
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