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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 Hits 41 – 60 of 104 41 Understanding the Cognitive Heterogeneity Associated with Autistic Traits: the Influence of Transdiagnostic Factors and Context ... Laurent, Eva. - : La Trobe, 2021 BASE Show details 42 SANKAT PRADATA AUR JANATA. ... Tomar, Neetusingh. - : figshare, 2021 BASE Show details 43 THE SIZE BIAS: DOES IT EXIST, AND HOW WOULD WE EXAMINE IT IN THE BRAIN ... Larranaga, Daniel Lucas. - : Purdue University Graduate School, 2021 BASE Show details 44 SANKAT PRADATA AUR JANATA. ... Tomar, Neetusingh. - : figshare, 2021 BASE Show details 45 SANKAT PRADATA AUR JANATA. ... Tomar, Neetusingh. - : figshare, 2021 BASE Show details 46 Meta-Analysis Data from 'A Role for Visual Memory in Vocabulary Development: A Systematic Review and Meta-Analysis' ... Pickering, Hayley; Peters, Jessica; Crewther, Sheila. - : La Trobe, 2021 BASE Show details 47 Εφαρμογές βαθιάς μάθησης ... : Applications of deep learning ... Τσερρίκου, Λέοναρντ. - : Πανεπιστήμιο Δυτικής Αττικής, 2021 BASE Show details 48 Αναγνώριση νοηματικής γλώσσας με τεχνικές βαθιάς μηχανικής μάθησης ... : Deep learning based sign language recognition ... Parelli, Maria. - : National Technological University of Athens, 2021 BASE Show details 49 The Phonological Latching Network Bratsvedal Collins, Joe Stephen In: BIOLINGUISTICS; Vol. 14 (2020): Special Issue—Biolinguistic Research in the 21st Century; 102-129 ; 1450-3417 (2021) BASE Show details 50 Discriminative feature modeling for statistical speech recognition ... Tüske, Zoltán. - : RWTH Aachen University, 2021 BASE Show details 51 Towards Learning Terminological Concept Systems from Multilingual Natural Language Text ... Wachowiak, Lennart; Lang, Christian; Heinisch, Barbara. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021 BASE Show details 52 How the input shapes the acquisition of verb morphology: elicited production and computational modelling in two highly inflected languages ... Engelmann, Felix. - : Open Science Framework, 2021 BASE Show details 53 Which Theory of Language for Deep Neural Networks? Speech and Cognition in Humans and Machines ... Capone, Luca. - : Technology and Language, 2(4), 29-60, 2021 BASE Show details 54 Data-Driven Analysis of Zebra Finch Song Copying and Learning Brudner, Samuel Navickas. - 2021 BASE Show details 55 Sentiment Analysis of Amazon Electronic Product Reviews using Deep Learning Abah, Jemimah Ojima. - : Dublin Business School, 2021 BASE Show details 56 Phonetic processing in speech sound disorder (Gerwin et al., 2021) ... Gerwin, Katelyn L.; Brosseau-Lapré, Françoise; Weber, Christine. - : ASHA journals, 2021 BASE Show details 57 Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor. ... Ravenscroft, Dafydd; Prattis, Ioannis; Kandukuri, Tharun. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 58 Phonetic processing in speech sound disorder (Gerwin et al., 2021) ... Gerwin, Katelyn L.; Brosseau-Lapré, Françoise; Weber, Christine. - : ASHA journals, 2021 BASE Show details 59 Automated Paraphrase Quality Assessment Using Language Models and Transfer Learning Bogdan Nicula; Mihai Dascalu; Natalie N. Newton; Ellen Orcutt; Danielle S. McNamara In: Computers; Volume 10; Issue 12; Pages: 166 (2021) Abstract: Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used to provide timely feedback to enhance learner paraphrasing skills more efficiently and effectively. Paraphrase identification is a popular NLP classification task that involves establishing whether two sentences share a similar meaning. Paraphrase quality assessment is a slightly more complex task, in which pairs of sentences are evaluated in-depth across multiple dimensions. In this study, we focus on four dimensions: lexical, syntactical, semantic, and overall quality. Our study introduces and evaluates various machine learning models using handcrafted features combined with Extra Trees, Siamese neural networks using BiLSTM RNNs, and pretrained BERT-based models, together with transfer learning from a larger general paraphrase corpus, to estimate the quality of paraphrases across the four dimensions. Two datasets are considered for the tasks involving paraphrase quality: ULPC (User Language Paraphrase Corpus) containing 1998 paraphrases and a smaller dataset with 115 paraphrases based on children’s inputs. The paraphrase identification dataset used for the transfer learning task is the MSRP dataset (Microsoft Research Paraphrase Corpus) containing 5801 paraphrases. On the ULPC dataset, our BERT model improves upon the previous baseline by at least 0.1 in F1-score across the four dimensions. When using fine-tuning from ULPC for the children dataset, both the BERT and Siamese neural network models improve upon their original scores by at least 0.11 F1-score. The results of these experiments suggest that transfer learning using generic paraphrase identification datasets can be successful, while at the same time obtaining comparable results in fewer epochs. Keyword: language models; natural language processing; paraphrase quality assessment; recurrent neural networks; transfer learning URL: https://doi.org/10.3390/computers10120166 BASE Hide details 60 Extracting Semantic Relationships in Greek Literary Texts Despina Christou; Grigorios Tsoumakas In: Sustainability ; Volume 13 ; Issue 16 (2021) BASE Show details Page: 1 2 3 4 5 6

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