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Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding.

, , , and . BioNLP@ACL, page 389-401. Association for Computational Linguistics, (2022)

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Controlling Politeness in Neural Machine Translation via Side Constraints., , and . HLT-NAACL, page 35-40. The Association for Computational Linguistics, (2016)Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases., , , and . EACL (Findings), page 674-683. Association for Computational Linguistics, (2024)Exploring Unsupervised Pretraining Objectives for Machine Translation., , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 2956-2971. Association for Computational Linguistics, (2021)Improving Neural Machine Translation Models with Monolingual Data., , and . ACL (1), The Association for Computer Linguistics, (2016)Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation., , and . WMT, page 32-42. The Association for Computer Linguistics, (2016)Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation., , , , , and . WMT, page 1393-1409. Association for Computational Linguistics, (2024)Toward Making the Most of Context in Neural Machine Translation., , , , and . CoRR, (2020)Generics are puzzling. Can language models find the missing piece?, , , and . CoRR, (2024)Survey of Low-Resource Machine Translation., , , , and . Comput. Linguistics, 48 (3): 673-732 (2022)No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement., , and . COLING, page 11121-11134. Association for Computational Linguistics, (2025)
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