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Cross-document Coreference Resolution over Predicted Mentions.

, , , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 5100-5107. Association for Computational Linguistics, (2021)

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Realistic Evaluation Principles for Cross-document Coreference Resolution., , , , and . *SEM, page 143-151. Association for Computational Linguistics, (2021)SpanBERT: Improving Pre-training by Representing and Predicting Spans, , , , , and . (Jul 24, 2019)How to train your self-supervised NLP model: Investigating pre-training objectives, data, and scale. University of Washington, USA, (2022)Spanbert: Improving pre-training by representing and predicting spans, , , , , and . Transactions of the Association for Computational Linguistics, (2020)pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference., , , , and . NAACL-HLT (1), page 3597-3608. Association for Computational Linguistics, (2019)FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary., , and . EACL, page 455-465. Association for Computational Linguistics, (2021)MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering., , , , , , , , and . ACL (1), page 12756-12770. Association for Computational Linguistics, (2023)Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities., , , , , , , and . ICCV, page 12031-12041. IEEE, (2023)pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference., , , , and . CoRR, (2018)DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions., , and . CoRR, (2021)