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StructuralLM: Structural Pre-training for Form Understanding.

, , , , , , and . ACL/IJCNLP (1), page 6309-6318. Association for Computational Linguistics, (2021)

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Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models., , , , and . NAACL-HLT, page 1716-1731. Association for Computational Linguistics, (2021)Aliababa DAMO Academy at TREC Precision Medicine 2020: State-of-the-art Evidence Retriever for Precision Medicine with Expert-in-the-loop Active Learning., , , , , , and . TREC, volume 1266 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)Improving Biomedical Pretrained Language Models with Knowledge., , , , and . BioNLP@NAACL-HLT, page 180-190. Association for Computational Linguistics, (2021)STRONGHOLD: Fast and Affordable Billion-Scale Deep Learning Model Training., , , , , , and . SC, page 71:1-71:17. IEEE, (2022)MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning., , , and . ICLR, OpenReview.net, (2021)Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation., , , , , and . ACL (1), page 14660-14679. Association for Computational Linguistics, (2023)PEER: Pre-training ELECTRA Extended by Ranking., , , and . ACL (Findings), page 6475-6491. Association for Computational Linguistics, (2023)XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding., , , , , , and . ACL (Findings), page 6368-6376. Association for Computational Linguistics, (2023)StructuralLM: Structural Pre-training for Form Understanding., , , , , , and . ACL/IJCNLP (1), page 6309-6318. Association for Computational Linguistics, (2021)Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach., , , , and . CoRR, (2018)