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Training Data Augmentation for Low-Resource Morphological Inflection.

, , , and . CoNLL Shared Task (1), page 31-39. Association for Computational Linguistics, (2017)

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Analyzing ASR Pretraining for Low-Resource Speech-to-Text Translation., , and . ICASSP, page 7909-7913. IEEE, (2020)Representational Bias in Unsupervised Learning of Syllable Structure., and . CoNLL, page 112-119. ACL, (2005)Modeling Graph Languages with Grammars Extracted via Tree Decompositions., , and . FSMNLP, page 54-62. The Association for Computer Linguistics, (2013)Unsupervised NLP and Human Language Acquisition: Making Connections to Make Progress.. ULNLP@EMNLP, page 1. Association for Computational Linguistics, (2011)A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability., , , and . EMNLP, page 42-54. ACL, (2013)Which Words Are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors that Increase ASR Error Rates., , and . ACL, page 380-388. The Association for Computer Linguistics, (2008)Semantic Parsing with Bayesian Tree Transducers., , and . ACL (1), page 488-496. The Association for Computer Linguistics, (2012)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)Evaluating computational models of infant phonetic learning across languages., , , , and . CogSci, cognitivesciencesociety.org, (2020)Unsupervised neural network based feature extraction using weak top-down constraints., , , and . ICASSP, page 5818-5822. IEEE, (2015)