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How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation.

, , , , , and . EMNLP, page 2122-2132. The Association for Computational Linguistics, (2016)

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Question Personalization in an Intelligent Tutoring System., , , , and . AIED (2), volume 13356 of Lecture Notes in Computer Science, page 586-590. Springer, (2022)How Useful Are Educational Questions Generated by Large Language Models?, , , and . AIED (Posters/Late Breaking Results/...), volume 1831 of Communications in Computer and Information Science, page 536-542. Springer, (2023)The RLLChatbot: a solution to the ConvAI challenge., , , , , , and . CoRR, (2018)How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation., , , , , and . EMNLP, page 2122-2132. The Association for Computational Linguistics, (2016)How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes., , , and . AAAI, page 23084-23091. AAAI Press, (2024)The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems., , , and . SIGDIAL Conference, page 285-294. The Association for Computer Linguistics, (2015)The Second Conversational Intelligence Challenge (ConvAI2)., , , , , , , , , and 7 other author(s). CoRR, (2019)Multi-modal Variational Encoder-Decoders., , , and . CoRR, (2016)Theano: A Python framework for fast computation of mathematical expressions, , , , , , , , , and 103 other author(s). (2016)cite arxiv:1605.02688Comment: 19 pages, 5 figures.Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus, , , , , , and . (2016)cite arxiv:1603.06807Comment: 13 pages, 1 figure, 7 tables.