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Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction.

, , , , , , , and . LREC, page 5565-5572. European Language Resources Association, (2022)

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Can Word Sense Distribution Detect Semantic Changes of Words?, , , , and . CoRR, (2023)Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings., , , and . ACL (student), page 138-147. Association for Computational Linguistics, (2021)Construction of Evaluation Dataset for Japanese Lexical Semantic Change Detection., , , and . PACLIC, page 125-136. Association for Computational Linguistics, (2023)Confidence Modeling for Neural Machine Translation., and . IALP, page 349-354. IEEE, (2019)Analyzing Semantic Changes in Japanese Words Using BERT., , and . PACLIC, page 270-280. Association for Computational Lingustics, (2021)Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings., and . ACL (Findings), page 6868-6882. Association for Computational Linguistics, (2023)Swap and Predict - Predicting the Semantic Changes in Words across Corpora by Context Swapping., and . EMNLP (Findings), page 7753-7772. Association for Computational Linguistics, (2023)A Word Sense Distribution-based approach for Semantic Change Prediction., , , , and . EMNLP (Findings), page 3575-3590. Association for Computational Linguistics, (2023)Estimating Machine Translation Quality of Any Input Sentence., and . Int. J. Asian Lang. Process., 30 (1): 2050002:1-2050002:16 (2020)A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection., and . CoRR, (2024)