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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 Hits 1 – 20 of 22 1 Entity-Enriched Neural Models for Clinical Question Answering Rawat, Bhanu Pratap Singh; Weng, Wei-Hung; Min, So Yeon... In: arXiv (2021) BASE Show details 2 Speakers Enhance Contextually Confusable Words Meinhardt, Eric; Bakovic, Eric; Bergen, Leon. - : eScholarship, University of California, 2020 BASE Show details 3 An empirical investigation of neural methods for content scoring of science explanations Riordan, Brian; Bichler, Sarah; Bradford, Allison. - : eScholarship, University of California, 2020 BASE Show details 4 Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations ... Coope, Sam; Farghly, Tyler; Gerz, Daniela. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 5 Efficient Intent Detection with Dual Sentence Encoders ... Casanueva, Inigo; Temcinas, Tadas; Gerz, Daniela; Henderson, Matthew; Vulic, Ivan; Linguist, Assoc Computat. - : Apollo - University of Cambridge Repository, 2020 Abstract: Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and ConveRT. We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings. In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset ... URL: https://dx.doi.org/10.17863/cam.53926 https://www.repository.cam.ac.uk/handle/1810/306835 BASE Hide details 6 Complementary Systems for Off-Topic Spoken Response Detection ... Raina, Vatsal; Gales, Mark; Knill, Katherine. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 7 Multidirectional Associative Optimization of Function-Specific Word Representations ... Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 8 Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter ... Conforti, Costanza; Berndt, Jakob; Pilehvar, Mohammad Taher. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 9 Investigating the Effect of Auxiliary Objectives for the Automated Grading of Learner English Speech Transcriptions ... Craighead, Hannah; Caines, Andrew; Buttery, Paula. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 10 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ... Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 11 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : 5TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2020), 2020 BASE Show details 12 Investigating the Effect of Auxiliary Objectives for the Automated Grading of Learner English Speech Transcriptions Yannakoudakis, Helen; Craighead, Hannah; Caines, Andrew. - : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 13 Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter Conforti, Costanza; Berndt, Jakob; Pilehvar, Mohammad Taher. - : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 14 Complementary Systems for Off-Topic Spoken Response Detection Raina, Vatsal; Gales, Mark; Knill, Katherine. - : Association for Computational Linguistics, 2020. : https://aclanthology.org/volumes/2020.bea-1/, 2020. : INNOVATIVE USE OF NLP FOR BUILDING EDUCATIONAL APPLICATIONS, 2020 BASE Show details 15 Multidirectional Associative Optimization of Function-Specific Word Representations Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Association for Computational Linguistics, 2020. : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 16 Efficient Intent Detection with Dual Sentence Encoders Casanueva, Inigo; Temcinas, Tadas; Gerz, Daniela. - : NLP FOR CONVERSATIONAL AI, 2020 BASE Show details 17 Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations Coope, Sam; Farghly, Tyler; Gerz, Daniela. - : Association for Computational Linguistics, 2020. : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 18 Tree-Structured Neural Topic Model Isonuma, Masaru; Mori, Junichiro; Bollegala, Danushka. - 2020 BASE Show details 19 GASC: Genre-Aware Semantic Change for Ancient Greek ... Perrone, Valerio; Palma, Marco; Hengchen, Simon. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 20 GASC: Genre-Aware Semantic Change for Ancient Greek Perrone, Valerio; Palma, Marco; Hengchen, Simon. - : Association for Computational Linguistics, 2019. : 1ST INTERNATIONAL WORKSHOP ON COMPUTATIONAL APPROACHES TO HISTORICAL LANGUAGE CHANGE, 2019 BASE Show details Page: 1 2

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