Modeling Mentor-Mentee Dialogues in Film

References

• Armand, J., G. Edouard, B. Piotr, and M. Tomas. 2016. Bag of tricks for efficient text classification. arXiv Preprint arXiv 1607:01759.
   Google Scholar
• Braun, V., and V. Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3 (2):77–101. doi: 10.1191/1478088706qp063oa.
   Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Conneau, A., D. Kiela, H. Schwenk, L. Barrault, and A. Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 conference on empirical methods in natural language processing, 670–680, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. URL https://www.aclweb.org/anthology/D17-1070.
   Google Scholar
• Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20 (3):273–97. doi: 10.1007/BF00994018.
   Web of Science ®Google Scholar
• Crammer, K., O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research 7:551–85.
   Web of Science ®Google Scholar
• David, B. 2013. Narration in the fiction film. Routledge
   Google Scholar
• Edwards, B. I., and A. D. Cheok. 2018. Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence:1–16.
   Web of Science ®Google Scholar
• Elliott, M., Hoey, and K. H. Kendrick. 2017. Conversation analysis. Research methods in psycholinguistics: A practical guide:151–73.
   Google Scholar
• Ensher, E. A., C. Thomas, and S. E. Murphy. 2001. Comparison of traditional, step-ahead, and peer mentoring on protégés’ support, satisfaction, and perceptions of career success: A social exchange perspective. Journal of Business and Psychology 15 (3):419–38.
   Web of Science ®Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: a gradient boosting machine. The Annals of Statistics 29 (5):1189–232. doi: 10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Hastie, T., S. Rosset, J. Zhu, and H. Zou. 2009. Multi-class adaboost. Statistics and Its Interface 2 (3):349–60. doi: 10.4310/SII.2009.v2.n3.a8.
   Web of Science ®Google Scholar
• Humberd, B. K., and E. D. Rouse. 2016. Seeing you in me and me in you: Personal identification in the phases of mentoring relationships. Academy of Management Review 41 (3):435–55. doi: 10.5465/amr.2013.0203.
   Web of Science ®Google Scholar
• Jeffrey, P., S. Richard, and M. Christopher. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532–1543.
   Google Scholar
• Kram, K. E. 1983. Phases of the mentor relationship. Academy of Management Journal 26 (4):608–25. doi: 10.5465/255910.
   Web of Science ®Google Scholar
• Kulik, J. A., and J. D. Fletcher. 2016. Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of Educational Research 86 (1):42–78. doi: 10.3102/0034654315581420.
   Web of Science ®Google Scholar
• Levinson, S. C. 1981. Some pre-observations on the modelling of dialogue. Discourse Processes 4 (2):93–116. doi: 10.1080/01638538109544510.
   Web of Science ®Google Scholar
• Levinson, S. C. 1992. Activity types and language. In Talk at work: Interaction in institutional settings, 66–100. Cambridge University Press.
   Google Scholar
• Levinson, S. C. 2013. Action formation and ascription. In The handbook of conversation analysis, 103–130. Wiley-Blackwell.
   Google Scholar
• Levinson, S. C. 2016. Turn-taking in human communication – origins and implications for language processing. Trends in Cognitive Sciences 20 (1):6–14.
   PubMed Web of Science ®Google Scholar
• Levinson, S. C., and F. Torreira. 2015. Timing in turn-taking and its implications for processing models of language. Frontiers in Psychology 6:731.
   PubMed Web of Science ®Google Scholar
• Lucas, K. F. 2001. The social construction of mentoring roles. Mentoring and Tutoring 9 (1):23–47. doi: 10.1080/13611260120046665.
   Google Scholar
• Matteo, P., G. Prakhar, and J. Martin. 2018. Unsupervised learning of sentence embeddings using compositional n-gram features. In NAACL 2018 – Conference of the North American Chapter of the Association for Computational Linguistics.
   Google Scholar
• Paul Grice, H. 1975. Logic and conversation:41–58.
   Google Scholar
• Paul, D., and H. John. 1992. Talk at work: Interaction in institutional settings. Cambridge Univ Pr,
   Google Scholar
• Payr, S., M. Skowron, A. Dobrosovestnova, M. Trapp, and R. Trappl. 2017. Strategic talk in film. Cybernetics and Systems 48 (8):576–96.
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg., et al. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12:2825–30.
   Web of Science ®Google Scholar
• Peter, H., and M. Jan. 2008. Mentors’ views about developing effective English teaching practices. Australian Journal of Teacher Education 33 (5):n5.
   Google Scholar
• Pfund, C., A. Byars-Winston, J. Branchaw, S. Hurtado, and K. Eagan. 2016. Defining attributes and metrics of effective research mentoring relationships. AIDS and Behavior 20 (S2):238–48. doi: 10.1007/s10461-016-1384-z.
   PubMed Web of Science ®Google Scholar
• Piotr, B., G. Edouard, J. Armand, and M. Tomas. 2016. Enriching word vectors with subword information. arXiv Preprint arXiv 1607:04606.
   Google Scholar
• Robinson, J. D. 2013. Overall structural organization. The handbook of conversation analysis:257–80.
   Google Scholar
• Rose Ragins, B., and K. E. Kram.2007. The roots and meaning of mentoring. The handbook of mentoring at work: Theory, research, and practice:3–15.
   Google Scholar
• Ryan, K., Z. Yukun, R. S. Ruslan, Z. Richard, U. Raquel, T. Antonio, and F. Sanja. 2015. Skip-thought vectors. Advances in Neural Information Processing Systems:3294–302.
   Google Scholar
• Schegloff, E. A. 2007. Sequence organization in interaction: Vol. 1. A primer in conversation analysis. Cambridge University Press.
   Google Scholar
• Skowron, M., M. Trapp, S. Payr, and R. Trappl. 2016. Automatic identification of character types from film dialogs. Applied Artificial Intelligence 30 (10):942–73.
   PubMed Web of Science ®Google Scholar
• Yukun, Z., K. Ryan, Z. Rich, S. Ruslan, U. Raquel, T. Antonio, and F. Sanja. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, 19–27.
   Google Scholar