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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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Genetic Neural Architecture Search for automatic assessment of human sperm images
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal.archives-ouvertes.fr/hal-03585035 ; Expert Systems with Applications, Elsevier, 2022 (2022)
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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
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In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
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In: Healthcare; Volume 10; Issue 3; Pages: 411 (2022)
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A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2398 (2022)
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Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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Abstract:
Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense reasoning. However, most pre-trained language models (PTLMs) do not consider commonsense. In addition, because the maximum number of tokens that a language model (LM) can deal with is limited, the entire dialogue history cannot be included. The resulting information loss has an adverse effect on performance. To address these problems, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR), a language model that exploits Semantic Search and continual learning. We used Semantic Search to complement information loss from truncated dialogue. In addition, we used Semantic Search and continual learning to improve the PTLM’s commonsense reasoning. Our model achieves an improvement of approximately 1.5% over the baseline method and can thus facilitate QA-related tasks. It contributes toward not only dialogue-based QA tasks but also another form of QA datasets for future tasks.
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Keyword:
commonsense reasoning; deep learning; dialogue-based multiple-choice QA; pre-trained language models; semantic search
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URL: https://doi.org/10.3390/app12094099
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Multimodal Lip-Reading for Tracheostomy Patients in the Greek Language
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In: Computers; Volume 11; Issue 3; Pages: 34 (2022)
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Identifying Learners’ Interaction Patterns in an Online Learning Community
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 4; Pages: 2245 (2022)
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Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases
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In: Diagnostics; Volume 12; Issue 4; Pages: 836 (2022)
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Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
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In: Diagnostics; Volume 12; Issue 4; Pages: 927 (2022)
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Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China
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In: Sustainability; Volume 14; Issue 5; Pages: 2923 (2022)
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Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review
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In: Diagnostics; Volume 12; Issue 4; Pages: 874 (2022)
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Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks
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In: Micromachines; Volume 13; Issue 4; Pages: 501 (2022)
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