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Connective Comprehension: An individual differences study ...
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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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7 |
Re-Evaluating Early Memorization of the Qurʾān in Medieval Muslim Cultures
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In: Religions; Volume 13; Issue 2; Pages: 179 (2022)
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Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
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A Deep Fusion Matching Network Semantic Reasoning Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3416 (2022)
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OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes
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In: Electronics; Volume 11; Issue 7; Pages: 1143 (2022)
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Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?
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In: Languages; Volume 7; Issue 2; Pages: 100 (2022)
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12 |
Design of an Artificial Intelligence of Things Based Indoor Planting Model for Mentha Spicata
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In: Processes; Volume 10; Issue 1; Pages: 116 (2022)
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Formalization of AMR Inference via Hybrid Logic Tableaux ...
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Child Social Understanding: How Theory of Mind Development is Influenced by Socio-Cultural Factors
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Neural-based Knowledge Transfer in Natural Language Processing
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17 |
Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation
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In: The Semantic Web: ESWC 2021 Satellite Events ; https://hal.inrae.fr/hal-03408000 ; Ruben Verborgh; Anastasia Dimou; Aidan Hogan; Claudia d'Amato; Ilaria Tiddi; Arne Bröring; Simon Mayer; Femke Ongenae; Riccardo Tommasini; Mehwish Alam. The Semantic Web: ESWC 2021 Satellite Events, 12739, Springer International Publishing, pp.176-185, 2021, Lecture Notes in Computer Science, 978-3-030-80417-6. ⟨10.1007/978-3-030-80418-3_30⟩ (2021)
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Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication
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20 |
How Do Language Intensity and Artificial Intelligence (AI) Affect Perceptions of Fact-checking Messages and Evaluations of Fact-checking Agencies?
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Xue, Haoning. - : eScholarship, University of California, 2021
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