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Search Challenges in Natural Language Generation with Complex Optimization Objectives, , , , and . KI - Künstliche Intelligenz, (to appear)Enhancing the Expression of Contrast in the SPaRKy Restaurant Corpus., , and . ENLG, page 30-39. The Association for Computer Linguistics, (2013)Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing., , and . INLG, page 183-194. Association for Computational Linguistics, (2020)Inducing clause-combining operations for natural language generation, and . Proc. of the 1st International Workshop on Data-to-Text Generation, Edinburgh, Scotland, UK, (2015)LOWRECORP: the Low-Resource NLG Corpus Building Challenge., , , , , , , and . INLG (Generation Challenges), page 1-9. Association for Computational Linguistics, (2023)Semantic Noise Matters for Neural Natural Language Generation., , and . INLG, page 421-426. Association for Computational Linguistics, (2019)What happens if you treat ordinal ratings as interval data? Human evaluations in NLP are even more under-powered than you think., and . EMNLP (1), page 8932-8939. Association for Computational Linguistics, (2021)From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation., , , , and . COLING, page 1524-1534. ACL, (2016)Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus., and . ENLG, page 28-37. The Association for Computer Linguistics, (2015)OTTers: One-turn Topic Transitions for Open-Domain Dialogue., , , and . ACL/IJCNLP (1), page 2492-2504. Association for Computational Linguistics, (2021)