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MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs.

, , , , , , , and . EMNLP (Demos), page 148-158. Association for Computational Linguistics, (2022)

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How to Generate a Good Word Embedding., , , and . IEEE Intelligent Systems, 31 (6): 5-14 (2016)Deep Semantic Hashing with Multi-Adversarial Training., , and . CIKM, page 1453-1462. ACM, (2018)Accurate Measurements of Wall Shear Stress on a Plate with Elliptic Leading Edge., , , , and . Sensors, 18 (8): 2682 (2018)Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing., , , , , and . Symmetry, 10 (12): 734 (2018)Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations., , , , , , , and . Remote Sensing, 10 (9): 1418 (2018)Exploring Distinctive Features in Distant Supervision for Relation Extraction., , , , and . AIRS, volume 8281 of Lecture Notes in Computer Science, page 344-355. Springer, (2013)BadNets: Evaluating Backdooring Attacks on Deep Neural Networks., , , and . IEEE Access, (2019)Curriculum Learning for Natural Answer Generation., , , and . IJCAI, page 4223-4229. ijcai.org, (2018)Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking., , , , and . CoRR, (2019)Conditional Generative Adversarial Networks for Commonsense Machine Comprehension., , and . IJCAI, page 4123-4129. ijcai.org, (2017)