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ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities
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In: ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22) ; https://hal-universite-paris-saclay.archives-ouvertes.fr/hal-03650618 ; 2022 (2022)
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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 3130 (2022)
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A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection
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In: Agriculture; Volume 12; Issue 2; Pages: 176 (2022)
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Translate Wisely! An Evaluation of Close and Adaptive Translation Procedures in an Experiment Involving Questionnaire Translation
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In: International journal of sociology ; 51 ; 2 ; 135-162 (2022)
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Perspective de la grammaire générative sur l’anaphore
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In: Corela, Vol 35 (2022) (2022)
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Narrow scoping content question items in shifty contexts: A case of surprising non-quotation in Uyghur
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In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5235 ; 2473-8689 (2022)
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Quantifying semantic and pragmatic effects on scalar diversity
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In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5216 ; 2473-8689 (2022)
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| 10 |
Preservice teacher’s purposeful questioning : a descriptive case study of elementary mathematics preservice teachers.
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| 11 |
English machine reading comprehension: new approaches to answering multiple-choice questions
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Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
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In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021)
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| 12 |
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
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| 13 |
The information-structural status of adjuncts: A QUD-based approach
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In: Discours ; https://halshs.archives-ouvertes.fr/halshs-03520607 ; Discours, 2021, 28 (2021)
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| 14 |
The Role of the Auditory and Visual Modalities in the Perceptual Identification of Brazilian Portuguese Statements and Echo Questions
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In: ISSN: 0023-8309 ; Language and Speech ; https://hal.archives-ouvertes.fr/hal-02456308 ; Language and Speech, SAGE Publications (UK and US), 2021, 64 (1), pp.3-23. ⟨10.1177/0023830919898886⟩ ; https://journals.sagepub.com/doi/pdf/10.1177/0023830919898886 (2021)
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| 15 |
A Multimodal Approach to the Discursive Construction of Stances in Political Debates in Hong Kong
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Geographic Question Answering with Spatially-Explicit Machine Learning Models
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Neural Question Answering Models with Broader Knowledge Scope and Deeper Reasoning Power
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| 19 |
Exploiting multimodality and structure in world representations ...
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Abstract:
An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, ... : DREAM CDT ...
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Keyword:
embodied learning; few-shot learning; graph classification; graph neural networks; graph pooling; language grounding; machine learning; meta-learning; neural processes; visual question answering
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URL: https://dx.doi.org/10.17863/cam.72490
https://www.repository.cam.ac.uk/handle/1810/325035
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