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Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction.

, and . ACL (1), page 2453-2463. Association for Computational Linguistics, (2019)

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Domain-Adaptive Pretraining Methods for Dialogue Understanding., , , , , and . ACL/IJCNLP (2), page 665-669. Association for Computational Linguistics, (2021)Unsupervised Learning of PCFGs with Normalizing Flow., , , , and . ACL (1), page 2442-2452. Association for Computational Linguistics, (2019)Self-Consistency Boosts Calibration for Math Reasoning., , , , , , , and . CoRR, (2024)Inconsistent dialogue responses and how to recover from them., , , , and . EACL (Findings), page 220-230. Association for Computational Linguistics, (2024)The Importance of Category Labels in Grammar Induction with Child-directed Utterances., and . IWPT 2020, page 145-150. Association for Computational Linguistics, (2020)Dynamic Augmentation Data Selection for Few-shot Text Classification., , , and . EMNLP (Findings), page 4841-4852. Association for Computational Linguistics, (2022)The Overall Markedness of Discourse Relations., and . EMNLP, page 1114-1119. The Association for Computational Linguistics, (2015)TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs., , , , , , , , , and 4 other author(s). CoRR, (2023)Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System., , , , and . BEA@EMNLP, page 11-21. Association for Computational Linguistics, (2017)Improving LLM Generations via Fine-Grained Self-Endorsement., , , , , , , and . ACL (Findings), page 8424-8436. Association for Computational Linguistics, (2024)