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Neural data-to-text generation: A comparison between pipeline and end-to-end architectures.

, , , and . EMNLP/IJCNLP (1), page 552-562. Association for Computational Linguistics, (2019)

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Human Language Technology and Communicative Disabilities: Requirements and Possibilities for the Future., , , , and . LREC, European Language Resources Association, (2010)Child-robot interaction during collaborative game play: effects of age and gender on emotion and experience., , , and . OZCHI, page 332-335. ACM, (2010)Engagement in longitudinal child-robot language learning interactions: Disentangling robot and task engagement., , , , , , , and . Int. J. Child Comput. Interact., (2022)Human evaluation of automatically generated text: Current trends and best practice guidelines., , , and . Comput. Speech Lang., (2021)Context sensitive generation of descriptions., and . ICSLP, ISCA, (1998)A Personalized Data-to-Text Support Tool for Cancer Patients., , , , , and . INLG, page 443-452. Association for Computational Linguistics, (2019)Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods., , and . INLG, page 35-45. Association for Computational Linguistics, (2018)Automatic analysis of semantic similarity in comparable text through syntactic tree matching., and . COLING, page 752-760. Tsinghua University Press, (2010)A New Model for Generating Multimodal Referring Expressions., and . ENLG@EACL, The Association for Computer Linguistics, (2003)Comparing Phrase-based and Syntax-based Paraphrase Generation., , , and . Monolingual@ACL, page 27-33. Association for Computational Linguistics, (2011)