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Challenges in the Automatic Analysis of Students' Diagnostic Reasoning., , , , , , , and . CoRR, (2018)Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains., , , , , , , and . ACL (1), page 2761-2772. Association for Computational Linguistics, (2019)Data-efficient Neural Text Compression with Interactive Learning., and . NAACL-HLT (1), page 2543-2554. Association for Computational Linguistics, (2019)Interactive Summarization of Large Document Collections., , and . HILDA@SIGMOD, page 9:1-9:4. ACM, (2019)Challenges in the Automatic Analysis of Students' Diagnostic Reasoning., , and . AAAI, page 6974-6981. AAAI Press, (2019)Manipulating the Difficulty of C-Tests., , and . ACL (1), page 360-370. Association for Computational Linguistics, (2019)Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation., , , and . IJCAI, page 2350-2356. ijcai.org, (2019)FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning., , , , , , , , , and . EMNLP/IJCNLP (3), page 73-78. Association for Computational Linguistics, (2019)MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance., , , , , and . EMNLP/IJCNLP (1), page 563-578. Association for Computational Linguistics, (2019)Better Rewards Yield Better Summaries: Learning to Summarise Without References., , , , , and . EMNLP/IJCNLP (1), page 3108-3118. Association for Computational Linguistics, (2019)