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Simplification of literary and scientific texts to improve reading fluency and comprehension in beginning readers of French
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In: ISSN: 0142-7164 ; EISSN: 1469-1817 ; Applied Psycholinguistics ; https://hal-amu.archives-ouvertes.fr/hal-03549026 ; Applied Psycholinguistics, Cambridge University Press (CUP), 2022, pp.1-28. ⟨10.1017/S014271642100062X⟩ (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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Literacy Acquisition Trajectories in Bilingual Language Minority Children and Monolingual Peers with Similar or Different SES: A Three-Year Longitudinal Study
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In: Brain Sciences; Volume 12; Issue 5; Pages: 563 (2022)
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Reading Strategy Intervention and Reading Comprehension Success in Bilingual Readers
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In: Electronic Thesis and Dissertation Repository (2022)
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READING COMPREHENSION CONSTRAINS WORD READING: A TONGUE TWISTER STUDY BY MODERATING ATTENTIONAL CONTROL
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In: Theses and Dissertations--Linguistics (2022)
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Improving EFL ninth graders’ reading comprehension through thieves learning strategy
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In: Journal of Applied Linguistics and Literature, Vol 7, Iss 1, Pp 232-258 (2022) (2022)
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Comprensión de lectura, reconocimiento de palabras y fluidez lectora en escolares de sexto año básico
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In: Onomázein: Revista de lingüística, filología y traducción de la Pontificia Universidad Católica de Chile, ISSN 0718-5758, Nº. 55, 2022, pags. 156-173 (2022)
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Neural-based Knowledge Transfer in Natural Language Processing
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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.
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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
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URL: http://hdl.handle.net/10315/39096
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| 9 |
INVESTIGATING THE COMPARATIVE EFFECTS OF SUSTAINED SILENT READING, ASSISTED REPEATED READING, AND TRADITIONAL READING
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In: TEFLIN Journal, Vol 33, Iss 1, Pp 173-200 (2022) (2022)
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Integrating Mind-Mapping Collaborated with Think-Pair-Share to Teach Reading Comprehension in Descriptive Text
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In: PAROLE: Journal of Linguistics and Education; Vol 12, No 1 (2022): Volume 12 Number 1 April 2022; 119-129 ; 23380683 ; 2087-345X (2022)
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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 |
Comprehension Monitoring: The Metacognitive Process of Reading Comprehension Examined via Eye-Movement Methodology
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| 13 |
Home attributes that relate to language and literacy attainments: A systematic review of studies from low- and middle-income countries ...
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| 14 |
Improving Second Language Acquisition of English Language Learners ...
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Vocabulary Scaffolding Features and Young Readers’ Comprehension of Digital Text: Insights from a Big Observational Dataset ...
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When Do Comprehenders Mentalize for Pragmatic Inference? A partial replication study. ...
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繪本閱讀教學研究──以國小一年級為對象 ; The Research of Picture Books Teaching Activity : 1st Graders of a Bilingual Flementary School Students as Research Subject
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