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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 Hits 1 – 6 of 6 1 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 2 Efficient Intent Detection with Dual Sentence Encoders ... Casanueva, Inigo; Temcinas, Tadas; Gerz, Daniela. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 3 Multidirectional Associative Optimization of Function-Specific Word Representations ... Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 4 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 5 Efficient Intent Detection with Dual Sentence Encoders Casanueva, Inigo; Temcinas, Tadas; Gerz, Daniela; Henderson, Matthew; Vulic, Ivan; Linguist, Assoc Computat. - : NLP FOR CONVERSATIONAL AI, 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 comprising 13,083 annotated examples over 77 intents. URL: https://doi.org/10.17863/CAM.53926 https://www.repository.cam.ac.uk/handle/1810/306835 BASE Hide details 6 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

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