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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 Hits 1 – 20 of 45 1 A review of Earth Artificial Intelligence Sun, Z; Sandoval, L; Crystal-Ornelas, R. - : eScholarship, University of California, 2022 BASE Show details 2 Psychometric properties of the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF): systematic review Poon, L.Y.J.; Tsang, H.W.H.; Chan, T.Y.J.. - : JMIR Publications, 2021 BASE Show details 3 Activated Epithelial FGF8 Signaling Induces Fused Supernumerary Incisors ... Chen, Y.; Wang, Z.; Lin, C.. - : SAGE Journals, 2021 BASE Show details 4 Activated Epithelial FGF8 Signaling Induces Fused Supernumerary Incisors ... Chen, Y.; Wang, Z.; Lin, C.. - : SAGE Journals, 2021 BASE Show details 5 Combining pre-trained word embeddings and linguistic features for sequential metaphor identification Mao, R.; Lin, C.; Guerin, F.. - 2021 BASE Show details 6 Interpreting verbal metaphors by paraphrasing Mao, R.; Lin, C.; Guerin, F.. - 2021 BASE Show details 7 Cross-lingual word embedding refinement by ℓ1 norm optimisation Peng, X.; Lin, C.; Stevenson, R.. - : The Association for Computational Linguistics, 2021 BASE Show details 8 Cross-lingual word embedding refinement by ℓ1 norm optimisation Peng, X.; Lin, C.; Stevenson, M.. - 2021 BASE Show details 9 Guiding the growth : difficulty-controllable question generation through step-by-step rewriting Cheng, Y.; Li, S.; Liu, B.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 10 Summarising historical text in modern languages Peng, X.; Zheng, Y.; Lin, C.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 11 Fast and scalable dialogue state tracking with explicit modular decomposition Wang, D.; Lin, C.; Liu, Q.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 12 Named entity aware transfer learning for biomedical factoid question answering Peng, K.; Yin, C.; Rong, W.. - : Institute of Electrical and Electronics Engineers (IEEE), 2021 BASE Show details 13 A text reassembling approach to natural language generation Li, X.; van Deemter, K.; Lin, C.. - 2020 BASE Show details 14 Revisiting the linearity in cross-lingual embedding mappings : from a perspective of word analogies Peng, X.; Lin, C.; Stevenson, M.. - 2020 BASE Show details 15 Improving variational autoencoder for text modelling with timestep-wise regularisation Li, R.; Li, X.; Chen, G.. - : Association for Computational Linguistics (ACL), 2020 BASE Show details 16 Split over-training for unsupervised purchase intention identification Yusof, N.F.A.; Lin, C.; Han, X.. - : The World Academy of Research in Science and Engineering, 2020 BASE Show details 17 DGST : a dual-generator network for text style transfer Li, X.; Chen, G.; Lin, C.. - : Association for Computational Linguistics (ACL), 2020 BASE Show details 18 Partner selection in sustainable supply chains: a fuzzy ensemble learning model Wu, C.; Lin, C.; Barnes, D.. - : Elsevier, 2020 BASE Show details 19 A dual-attention hierarchical recurrent neural network for dialogue act classification Li, R.; Lin, C.; Collinson, M.; Li, X.; Chen, G.. - : Association for Computational Linguistics (ACL), 2019 Abstract: Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets. URL: https://eprints.whiterose.ac.uk/177032/ https://eprints.whiterose.ac.uk/177032/7/K19-1036.pdf https://aclanthology.org/K19-1036 BASE Hide details 20 A stable variational autoencoder for text modelling Li, R.; Li, X.; Lin, C.. - : Association for Computational Linguistics (ACL), 2019 BASE Show details Page: 1 2 3

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