Papers
Topics
Authors
Recent
Search
2000 character limit reached

"You might think about slightly revising the title": identifying hedges in peer-tutoring interactions

Published 18 Jun 2023 in cs.CL and cs.AI | (2306.14911v1)

Abstract: Hedges play an important role in the management of conversational interaction. In peer tutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with students in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of such a hybrid model approach.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. Heike Adel and Hinrich Schütze. 2017. Exploring different dimensions of attention for uncertainty detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 22–34, Valencia, Spain. Association for Computational Linguistics.
  2. Shoshana Blum-Kulka. 1987. Indirectness and politeness in requests: Same or different? Journal of pragmatics, 11(2):131–146.
  3. Penelope Brown and Stephen C Levinson. 1987. Politeness: Some universals in language usage, volume 4. Cambridge university press.
  4. Judee K Burgoon and Randall J Koper. 1984. Nonverbal and relational communication associated with reticence. Human Communication Research, 10(4):601–626.
  5. Understanding global feature contributions with additive importance measures. Advances in Neural Information Processing Systems, 33:17212–17223.
  6. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186.
  7. The conll-2010 shared task: learning to detect hedges and their scope in natural language text. In Proceedings of the fourteenth conference on computational natural language learning–Shared task, pages 1–12.
  8. Bruce Fraser. 2010. Pragmatic competence: The case of hedging. New approaches to hedging, 1534.
  9. Viola Ganter and Michael Strube. 2009. Finding hedges by chasing weasels: Hedge detection using wikipedia tags and shallow linguistic features. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 173–176.
  10. “i think it might help if we multiply, and not add”: Detecting indirectness in conversation. In 9th International Workshop on Spoken Dialogue System Technology, pages 27–40. Springer.
  11. Shifting the load: a peer dialogue agent that encourages its human collaborator to contribute more to problem solving. International Journal of Artificial Intelligence in Education, 27:101–129.
  12. Ken Hyland. 1998. Hedging in scientific research articles, volume 54. John Benjamins Publishing.
  13. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30:3146–3154.
  14. Klaus Krippendorff. 2004. Reliability in content analysis: Some common misconceptions and recommendations. Human communication research, 30(3):411–433.
  15. George Lakoff. 1975. Hedges: A study in meaning criteria and the logic of fuzzy concepts. In Contemporary research in philosophical logic and linguistic semantics, pages 221–271. Springer.
  16. Matthew Leach. 2005. Rapport: A key to treatment success. Complementary therapies in clinical practice, 11:262–5.
  17. Ilya Loshchilov and Frank Hutter. 2018. Decoupled weight decay regularization. In International Conference on Learning Representations.
  18. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888.
  19. Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems, pages 4768–4777.
  20. Howard Lune and Bruce L Berg. 2017. Qualitative research methods for the social sciences. Pearson.
  21. The impact of peer tutors’ use of indirect feedback and instructions. In Proceedings of the International Society of the Learning Sciences. ISLS.
  22. Ben Medlock and Ted Briscoe. 2007. Weakly supervised learning for hedge classification in scientific literature. In Proceedings of the 45th annual meeting of the association of computational linguistics, pages 992–999.
  23. Christoph Molnar. 2020. Interpretable machine learning. Lulu. com.
  24. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32:8026–8037.
  25. The development and psychometric properties of liwc2015. Technical report, University of Texas at Austin.
  26. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
  27. On hedging in physician-physician discourse. Linguistics and the Professions, 8(1):83–97.
  28. Anna Prokofieva and Julia Hirschberg. 2014. Hedging and speaker commitment. In 5th Intl. Workshop on Emotion, Social Signals, Sentiment & Linked Open Data, Reykjavik, Iceland.
  29. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992.
  30. Cognitive-inspired conversational-strategy reasoner for socially-aware agents. In IJCAI, pages 3807–3813.
  31. Tim Rowland. 2007. ‘well maybe not exactly, but it’s around fifty basically?’: Vague language in mathematics classrooms. In Vague language explored, pages 79–96. Springer.
  32. Mind your ps and qs! how polite instructions affect learning with multimedia. Computers in Human Behavior, 51:546–555.
  33. Tanmay Sinha and Justine Cassell. 2015. We click, we align, we learn: Impact of influence and convergence processes on student learning and rapport building. In Proceedings of the 1st Workshop on Modeling INTERPERsonal SynchrONy And InfLuence, INTERPERSONAL ’15, page 13–20, New York, NY, USA. Association for Computing Machinery.
  34. Helen Spencer-Oatey. 2005. (im)politeness, face and perceptions of rapport: Unpackaging their bases and interrelationships. Journal of Politeness Research. Language, Behaviour, Culture, 1(1):95–119.
  35. Cross-genre and cross-domain detection of semantic uncertainty. Computational Linguistics, 38(2):335–367.
  36. A cascade method for detecting hedges and their scope in natural language text. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning–Shared Task, pages 13–17.
  37. Using hedge detection to improve committed belief tagging. In Proceedings of the Workshop on Computational Semantics beyond Events and Roles, pages 1–5.
  38. Teppo Varttala. 1999. Remarks on the communicative functions of hedging in popular scientific and specialist research articles on medicine. English for specific purposes, 18(2):177–200.
  39. Attention is all you need. In Advances in neural information processing systems, pages 5998–6008.
  40. Erik Velldal. 2011. Predicting speculation: a simple disambiguation approach to hedge detection in biomedical literature. Journal of Biomedical Semantics, 2(5):1–14.
  41. Veronika Vincze. 2014. Uncertainty detection in natural language texts. PhD, University of Szeged, page 141.
  42. The politeness effect: Pedagogical agents and learning outcomes. International journal of human-computer studies, 66(2):98–112.
  43. Shawn Warner-Garcia. 2014. Laughing when nothing’s funny: The pragmatic use of coping laughter in the negotiation of conversational disagreement. Pragmatics, 24(1):157–180.
  44. Timothy Williamson. 2002. Vagueness. Routledge.
  45. Towards a dyadic computational model of rapport management for human-virtual agent interaction. In International Conference on Intelligent Virtual Agents, pages 514–527. Springer.
  46. Automatic recognition of conversational strategies in the service of a socially-aware dialog system. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 381–392.
  47. Socially-aware virtual agents: Automatically assessing dyadic rapport from temporal patterns of behavior. In International conference on intelligent virtual agents, pages 218–233. Springer.
Citations (13)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up