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A Structured Syntax-Semantics Interface for English-AMR Alignment.

, , and . NAACL-HLT, page 1169-1180. Association for Computational Linguistics, (2018)

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The problem with probabilistic DAG automata for semantic graphs., , and . CoRR, (2018)Putting Human Assessments of Machine Translation Systems in Order.. WMT@NAACL-HLT, page 1-9. The Association for Computer Linguistics, (2012)978-1-937284-20-6.Pre-training on high-resource speech recognition improves low-resource speech-to-text translation., , , , and . NAACL-HLT (1), page 58-68. Association for Computational Linguistics, (2019)Word-Based Alignment, Phrase-Based Translation: What's the Link?, and . AMTA, page 90-99. Association for Machine Translation in the Americas, (2006)Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice., , and . ACL (1), page 6738-6758. Association for Computational Linguistics, (2022)First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models., , , and . NAACL-HLT, page 2310-2326. Association for Computational Linguistics, (2024)Universal Dependencies to Logical Forms with Negation Scope., , , and . CoRR, (2017)Understanding Learning Dynamics Of Language Models with SVCCA., and . CoRR, (2018)'Indicatements' that character language models learn English morpho-syntactic units and regularities., and . BlackboxNLP@EMNLP, page 145-153. Association for Computational Linguistics, (2018)What do character-level models learn about morphology? The case of dependency parsing., , and . EMNLP, page 2573-2583. Association for Computational Linguistics, (2018)