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VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation., , , , , , , and . CoRR, (2020)From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression., , , , , , and . CoRR, (2021)Enhancing Freebase Question Answering Using Textual Evidence., , , , and . CoRR, (2016)Achieving Human Parity on Visual Question Answering., , , , , , , , , and 7 other author(s). CoRR, (2021)Nested Named Entity Recognition with Partially-Observed TreeCRFs., , , , and . CoRR, (2020)Biomedical Question Answering: A Survey of Approaches and Challenges., , , , , , , , , and . ACM Comput. Surv., 55 (2): 35:1-35:36 (2023)Alibaba DAMO Academy at TREC Clinical Trials 2021: ExploringEmbedding-based First-stage Retrieval with TrialMatcher., , , , and . TREC, volume 500-335 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2021)Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling., , , and . EMNLP, page 536-540. The Association for Computational Linguistics, (2015)VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation., , , , , , , and . ACL/IJCNLP (1), page 3980-3994. Association for Computational Linguistics, (2021)Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction., , , , and . ACL/IJCNLP (2), page 942-947. Association for Computational Linguistics, (2021)