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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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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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Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
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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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Exploiting multimodality and structure in world representations ...
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Enhancing multiple-choice question answering with causal knowledge
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ViquiQuAD: an extractive QA dataset from Catalan Wikipedia ...
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ViquiQuAD: an extractive QA dataset from Catalan Wikipedia ...
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VilaQuAD: an extractive QA dataset from Catalan newswire ...
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VilaQuAD: an extractive QA dataset from Catalan newswire ...
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CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions ...
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Ates, Tayfun; Atesoglu, M. Samil; Yigit, Cagatay; Kesen, Ilker; Kobas, Mert; Erdem, Erkut; Erdem, Aykut; Goksun, Tilbe; Yuret, Deniz. - : Zenodo, 2021
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Abstract:
Humans are able to perceive, understand and reason about physical events. Developing models with similar physical understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this goal, in this work, we introduce CRAFT, a new visual question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories from CRAFT include previously studied descriptive and counterfactual questions. Besides, inspired by the theories of force dynamics in cognitive linguistics, we introduce new question categories that involve understanding the interactions of objects through the notions of cause , enable , and prevent . Our results demonstrate that even though these tasks seem to be simple and intuitive for humans, the evaluated baseline ... : CRAFT was supported in part by GEBIP 2018 Award of the Turkish Academy of Sciences to E. Erdem and T. Goksun, BAGEP 2021 Award of the Science Academy to A. Erdem, and AI Fellowship to Ilker Kesen provided by the KUIS AI Lab. ...
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Keyword:
causal reasoning; force dynamics; intuitive physics; temporal reasoning; visual question answering
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URL: https://zenodo.org/record/4904783 https://dx.doi.org/10.5281/zenodo.4904783
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CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions ...
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Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ...
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WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
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Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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