Results 31 to 40 of about 335 (211)
Benchmarking Machine Reading Comprehension: A Psychological Perspective [PDF]
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021 Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding. However, the conventional task design of MRC lacks explainability beyond the model interpretation, i.e., reading comprehension by a model cannot be explained in human terms.
Saku Sugawara +2 more
openaire +3 more sources
Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions [PDF]
مجله علوم روانشناختی, 2023 Background: The intersection of artificial intelligence (AI), psychology and applied linguistics particularly in the realm of language learning, has opened up a fascinating avenue for exploring the intricate processes and mechanisms underlying human ...
Nora Darjazini +3 more
doaj
Applied Sciences, 2022 Machine reading comprehension (MRC) is an important research topic in the field of Natural Language Processing (NLP). However, traditional MRC models often face challenges of information loss, lack of capability to retain long-distance dependence, and ...
Nawei Shi +2 more
doaj +1 more source
Improving Machine Reading Comprehension with General Reading Strategies [PDF]
Proceedings of the 2019 Conference of the North, 2019 Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is resource-demanding to impart rich general domain knowledge into a deep language model via pre-training.
Kai Sun 0006 +3 more
openaire +4 more sources
Answer Span Correction in Machine Reading Comprehension [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2020, 2020 Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the "answerability" of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct ...
Revanth Gangi Reddy +5 more
openaire +3 more sources
VisualMRC: Machine Reading Comprehension on Document Images
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Recent studies on machine reading comprehension have focused on text-level understanding but have not yet reached the level of human understanding of the visual layout and content of real-world documents. In this study, we introduce a new visual machine reading comprehension dataset, named VisualMRC, wherein given a question and a document image, a ...
Ryota Tanaka +2 more
openaire +4 more sources
Robust Domain Adaptation for Machine Reading Comprehension
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., Noisy Correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during ...
Liang Jiang +4 more
openaire +3 more sources
Exploring Machine Reading Comprehension for Continuous Questions via Subsequent Question Completion
IEEE Access, 2021 In recent years, the Sq-MRC (machine reading comprehension for separate questions) task, where the questioner poses a separate question each time, has experienced rapid development.
Kaijing Yang, Xin Zhang, Dongmei Chen
doaj +1 more source
Sogou Machine Reading Comprehension Toolkit
CoRR, 2019 Machine reading comprehension have been intensively studied in recent years, and neural network-based models have shown dominant performances. In this paper, we present a Sogou Machine Reading Comprehension (SMRC) toolkit that can be used to provide the fast and efficient development of modern machine comprehension models, including both published ...
Jindou Wu +7 more
openaire +2 more sources
Knowledge Based Machine Reading Comprehension
CoRR, 2018 Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability.
Yibo Sun +6 more
openaire +3 more sources