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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 – 7 of 7 1 NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jiang, Jing; Lim, Ee-Peng. - : Underline Science Inc., 2021 BASE Show details 2 NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ... Zhang, Qiyuan; Wang, Lei; Yu, Sicheng. - : arXiv, 2021 BASE Show details 3 ENCONTER: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer ... Hsieh, Lee-Hsun; Lee, Yang-Yin; Lim, Ee-Peng. - : arXiv, 2021 Abstract: Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences. However, these models do not perform well under hard lexical constraints as they lack fine control of content generation process. Progressive insertion-based transformers can overcome the above limitation and efficiently generate a sequence in parallel given some input tokens as constraint. These transformers however may fail to support hard lexical constraints as their generation process is more likely to terminate prematurely. The paper analyses such early termination problems and proposes the Entity-constrained insertion transformer (ENCONTER), a new insertion transformer that addresses the above pitfall without compromising much generation efficiency. We introduce a new training strategy that considers predefined hard lexical constraints (e.g., entities to be included in the generated sequence). Our experiments show that ENCONTER outperforms other baseline models in several performance ... : EACL 2021 long paper ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2103.09548 https://arxiv.org/abs/2103.09548 BASE Hide details 4 On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation ... Silva, Amila; Lo, Pei-Chi; Lim, Ee-Peng. - : arXiv, 2020 BASE Show details 5 Changing with time:modelling and detecting user lifecycle periods in online community platforms Rowe, Matthew. - : Springer-Verlag, 2013 BASE Show details 6 Performance measurement framework for hierarchical text classification Sun, Aixin; Lim, Ee-Peng; Ng, Wee Keong In: American Society for Information Science and Technology. Journal of the American Society for Information Science and Technology. - New York, NY : Wiley 54 (2003) 11, 1014-1028 BLLDB Show details 7 Entity Identification in Database Integration Lim, Ee-Peng; Srivastava, Jaideep; Prabhakar, Satya... In: Information sciences. - New York, NY : Elsevier Science Inc. 89 (1996) 1-2, 1-38 OLC Linguistik Show details

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