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Temporal Effects on Pre-trained Models for Language Processing Tasks.

, and . Trans. Assoc. Comput. Linguistics, (2022)

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Predicting Summary Quality using Limited Human Input., and . TAC, NIST, (2009)Automatic sense prediction for implicit discourse relations in text., , and . ACL/IJCNLP, page 683-691. The Association for Computer Linguistics, (2009)Factual or Contextual? Disentangling Error Types in Entity Description Generation., , and . ACL (1), page 8322-8340. Association for Computational Linguistics, (2023)Aggregating and Predicting Sequence Labels from Crowd Annotations., , , , and . ACL (1), page 299-309. Association for Computational Linguistics, (2017)Measuring Importance and Query Relevance in Topic-focused Multi-document Summarization., , and . ACL, The Association for Computational Linguistics, (2007)Can You Summarize This? Identifying Correlates of Input Difficulty for Multi-Document Summarization., and . ACL, page 825-833. The Association for Computer Linguistics, (2008)Influence Functions for Sequence Tagging Models., , , and . EMNLP (Findings), page 824-839. Association for Computational Linguistics, (2022)Evaluating Multiple System Summary Lengths: A Case Study., , , , , , and . EMNLP, page 774-778. Association for Computational Linguistics, (2018)Automatically Evaluating Content Selection in Summarization without Human Models., and . EMNLP, page 306-314. ACL, (2009)UniDoc: Unified Pretraining Framework for Document Understanding., , , , , , , and . NeurIPS, page 39-50. (2021)