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1	tptp-utils 1.1 ...
	Steen, Alexander. - : Zenodo, 2022
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2	tptp-utils 1.1 ...
	Steen, Alexander. - : Zenodo, 2022
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3	Connective Comprehension: An individual differences study ...
	Scholman, Merel. - : Open Science Framework, 2022
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4	Computing Entailments Online, ver. 5 ...
	Dąbkowski, Maksymilian. - : Open Science Framework, 2022
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5	Computing Entailments Online, ver. 6 ...
	Dąbkowski, Maksymilian. - : Open Science Framework, 2022
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6	Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
	Seungwon Jeong; Dongsuk Oh; Kinam Park; Heuiseok Lim
	In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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7	Re-Evaluating Early Memorization of the Qurʾān in Medieval Muslim Cultures
	Essam Ayyad
	In: Religions; Volume 13; Issue 2; Pages: 179 (2022)
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8	Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
	Yu-Jie Zhu; Wei Guo; Hu-Chen Liu
	In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
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9	A Deep Fusion Matching Network Semantic Reasoning Model
	Wenfeng Zheng; Yu Zhou; Shan Liu; Jiawei Tian; Bo Yang; Lirong Yin
	In: Applied Sciences; Volume 12; Issue 7; Pages: 3416 (2022)
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10	OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes
	Hubert Kenfack Ngankam; Hélène Pigot; Sylvain Giroux
	In: Electronics; Volume 11; Issue 7; Pages: 1143 (2022)
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11	Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?
	Dean D’Souza; Yousra Dakhch
	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
	Hao-Hsiang Ku; Cheng-Hsuan Liu; Wen-Cheng Wang
	In: Processes; Volume 10; Issue 1; Pages: 116 (2022)
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13	Formalization of AMR Inference via Hybrid Logic Tableaux ...
	Goldner, Eli Tecumseh. - : Brandeis University, 2022
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14	Computation, Rule Following, and Ethics in AIs
	Seo, Hyunjin; Thorson, Stuart. - 2022
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15	Child Social Understanding: How Theory of Mind Development is Influenced by Socio-Cultural Factors
	Totolici, Maria. - : University of Otago, 2022
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16	Neural-based Knowledge Transfer in Natural Language Processing
	Wang, Chao. - 2022
	Abstract: In Natural Language Processing (NLP), neural-based knowledge transfer, which is to transfer out-of-domain (OOD) knowledge to task-specific neural networks, has been applied to many NLP tasks. To further explore neural-based knowledge transfer in NLP, in this dissertation, we consider both structured OOD knowledge and unstructured OOD knowledge, and deal with several representative NLP tasks. For structured OOD knowledge, we study the neural-based knowledge transfer in Machine Reading Comprehension (MRC). In single-passage MRC tasks, to bridge the gap between MRC models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, we integrate the neural networks of MRC models with the general knowledge of human beings embodied in knowledge bases. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose a novel MRC model named Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. According to the experimental results, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. On top of that, when only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise. In multi-hop MRC tasks, to probe the strength of Graph Neural Networks (GNNs), we propose a novel multi-hop MRC model named Graph Aided Reader (GAR), which uses GNN methods to perform multi-hop reasoning, but is free of any pre-trained language model and completely end-to-end. For graph construction, GAR utilizes the topic-referencing relations between passages and the entity-sharing relations between sentences, which is aimed at obtaining the most sensible reasoning clues. For message passing, GAR simulates a top-down reasoning and a bottom-up reasoning, which is aimed at making the best use of the above obtained reasoning clues. According to the experimental results, GAR even outperforms several competitors relying on pre-trained language models and filter-reader pipelines, which implies that GAR benefits a lot from its GNN methods. On this basis, GAR can further benefit from applying pre-trained language models, but pre-trained language models can mainly facilitate the within-passage reasoning rather than cross-passage reasoning of GAR. Moreover, compared with the competitors constructed as filter-reader pipelines, GAR is not only easier to train, but also more applicable to the low-resource cases. For unstructured OOD knowledge, we study the neural-based knowledge transfer in Natural Language Understanding (NLU), and focus on the neural-based knowledge transfer between languages, which is also known as Cross-Lingual Transfer Learning (CLTL). To facilitate the CLTL of NLU models, especially the CLTL between distant languages, we propose a novel CLTL model named Translation Aided Language Learner (TALL), where CLTL is integrated with Machine Translation (MT). Specifically, we adopt a pre-trained multilingual language model as our baseline model, and construct TALL by appending a decoder to it. On this basis, we directly fine-tune the baseline model as an NLU model to conduct CLTL, but put TALL through an MT-oriented pre-training before its NLU-oriented fine-tuning. To make use of unannotated data, we implement the recently proposed Unsupervised Machine Translation (UMT) technique in the MT-oriented pre-training of TALL. According to the experimental results, the application of UMT enables TALL to consistently achieve better CLTL performance than the baseline model without using more annotated data, and the performance gain is relatively prominent in the case of distant languages.
	Keyword: Cross-lingual transfer learning; Graph neural network; Information technology; Knowledge base; Knowledge graph; Knowledge transfer; Machine Reading Comprehension; Multi-hop reasoning; Natural Language Processing; Natural language understanding; Neural network; unsupervised machine translation
	URL: http://hdl.handle.net/10315/39096
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17	Ranking Semantics for Argumentation Systems With Necessities
	Doder, Dragan; Vesic, Srdjan; Croitoru, Madalina
	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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18	Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation
	Vargas-Rojas, Luis Felipe
	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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19	Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication
	Zheng, Zilong. - : eScholarship, University of California, 2021
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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?
	Xue, Haoning. - : eScholarship, University of California, 2021
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