Time & Location: decided at kick-off and via Doodle, indicatively end of March-beginning April
Teacher: Dr Volha Petukhova
Registration in LSF
Suitable for: Bachelor CoLi, CS
Pragmatics as a branch of linguistics can be characterized as the study of language use in context and concerns with interpretation of utterance meaning in context. Computational pragmatics is pragmatics with computational means, which include models of dialogue management processes, collections of language use data, annotation schemes and software tools for corpus creation, process models of language generation and interpretation, context representations, and inference methods for context-dependent utterance generation and interpretation processes. Work on computational pragmatics often takes place within research on dialogue systems.
In this proseminar, we will learn how to perform research in computational pragmatics: (1) understand how to compute pragmatic meaning; (2) study the mechanisms underlying the main pragmatic inferences and aspects of pragmatic meaning; (3) discuss algorithms that enable the use of theoretical concepts in practical applications. Focus will be put on computational dialogue modelling for dialogue system design.
Seminar meetings will take in March-April. After paper selection/assignment, each student will give a presentation on a selected topic (30 minutes + 10 minutes questions): dry-runs (ungraded) and final (graded). At the end of the course, each student has to prepare a short report (about 10 pages) and hand it for grading.
Grading: 40% based on the talk, 20% based on discussion participation and assignments; 40% based on the report
TOPICS and SUGGESTED PAPERS
Situating Computational Pragmatics
Introduction slides: in Teams
- Bunt, Harry (2015) Computational Pragmatics. Chapter 19, Oxford Handbook of Pragmatics, Yan Huang, editor. Oxford University Press.
- Bunt, Harry and William Black (2000) The ABC of Computational Pragmatics. In Harry Bunt and William Black (eds.) Abduction, Belief and Context in Dialogue. Studies in Computational Pragmatics. Amsterdam: Benjamins, pp. 1-46. Shared at ResearchGate
- Jurafsky, Daniel, and James H. Martin. 2009. Speech and language processing: An introduction to natural language processing, speech recognition, and computational linguistics. Chapters 24 & 25 of the online 3rd edition draft.
1. Dialogue Interaction and Conversational Analysis
- Adjacency pairs: Schegloff, E. (1968). Sequencing in conversational openings. American Anthropologist, 70:1075–1095
- Turn-taking behavior: Sacks, H., Schegloff, E., and Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4):696–735
2. Activity-Based Approach to Pragmatics
- Searle, J. (1975a). The structure of illocutionary acts. University of Minesota Press.
- Searle, J. (1975b). A taxonomy of illocutionary acts. University of Minesota Press.
- Allwood, J. (1977). A critical look at speech act theory. Logic, Pragmatics and Grammar, pages 53–69.
- Allwood, J. (2000). An activity-based approach to pragmatics. Abduction, Belief and Context in Dialogue, pages 47–81.
- Bunt, H. (2000). Dialogue pragmatics and context specification. In Bunt, H. and Black, W., editors, Abduction, Belief and Context in Dialogue; studies in computational pragmatics, pages 81–105. John Benjamins, Amsterdam.
3. Dialogue Acts and Context
- Harry Bunt (2011). Interpretation and generation of dialogue with multidimensional context models. In: A. Esposito (ed.) (Toward Autonomous, Adaptive, and Context-Aware Multimedia Interfaces: Theoretical and Practical Issues. Berlin: Springer 2011, pp. 214-242
- Harry Bunt(2011). The Semantics of Dialogue Acts. In Proceedings 9th International Conference on Computational Semantics (IWCS 2011), Oxford, UK January 12-14, 2011, pp. 1-14.
4. Feedback and Grounding
- Allwood, J., Nivre, J., and E, A. (1993). On the semantics and pragmatics of linguistic feedback. Journal of Semantics, 9-1:1–26
- Clark, H. and Schaefer, E. (1989). Contributing to discourse. Cognitive Science, 13:259–294.
- Traum, D. (1999). Computational models of grounding in collaborative systems. In Brennen, S.E. Giboin, A. and Traum, D., editors, Working Papers of the AAAI Fall Symposium on Psychological Models of Communication in Collaborative Systems, pages 124–131.
- Harry Bunt, Roser Morante, and Simon Keizer (2007). An empirically based computational model of grounding in dialogue. In Proceedings of the Eighth SIGDIAL Conference on Discourse and Dialogue (SIGDIAL 2007). Antwerp, pp. 283-290.
5.Computational Dialogue Models
- Ginzburg, J., Fernandez, R. (2010). Computational Models of Dialogue. The handbook of computational linguistics and natural language processing, 57, 1.
- Van Zanten, G. V. (1996). Pragmatic interpretation and dialogue management in spoken-language systems. In Proceedings of the Twente Workshop on Language Technology.
- Allen, J., Ferguson, G., and Stent, A. (2001). An architecture for more realistic conversational systems. In Proceedings of the 6th International Conference on Intelligent User Interfaces (IUI’01), Santa Fe, New Mexico, USA, pages 1–8.
- Bos, J., Klein, E., Lemon, O., and Oka, T. (2003). DIPPER: description and formalisation of an information-state update dialogue system architecture. In Proceedings of the 4th SIGdial Workshop on Discourse and Dialogue, pages 115–124.
- Bohus, D., & Rudnicky, A. I. (2009). The RavenClaw dialog management framework: Architecture and systems. Computer Speech & Language, 23(3), 332-361.
6. Standards and Resources for Data-Driven Dialogue Modelling
- Bunt et al. (2010). Towards and ISO standard for dialogue act annotation. In Proceedings of LREC 2010, the Seventh International Conference on Language Resources and Evaluation, Malta, May 16-23, 2010. Paris: ELDA, pp. 2548-2558.
- Serban et al. (2015). A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742.
7. Machine Learning in Dialogue Modelling: Dialogue Act Recognition
- Stolcke et al. (2000). Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26(3):339–373.
- Kumar et al. (2018). Dialogue act sequence labeling using hierarchical encoder with CRF. In Thirty-Second AAAI Conference on Artificial Intelligence.
8. Machine Learning in Dialogue Modelling: Statistical and Neural Models
- Young et al. (2013). POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101(5):1160–1179.
- Serban et al. (2015). Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In Thirtieth AAAI Conference on Artificial Intelligence.
9. Authoring tools and development environments
- Gabriel Skantze and Samer Al Moubayed (2012) Iristk: a statechart-based toolkit for multi-party face-to-face interaction. In Proceedings of the 14th ACM international conference on Multimodal interaction, pages 69–76.
- Alexander I Rudnicky, Christina Bennett, Alan W Black, Ananlada Chotomongcol, Kevin Lenzo, Alice Oh, and Rita Singh. (2020) Task and domain specific modelling in the Carnegie Mellon Communicator system. Technical report, CMU, Pittsburgh PA School of Computer Science.
- Youngsoo Jang, Jongmin Lee, Jaeyoung Park, Kyeng-Hun Lee, Pierre Lison, and Kee-Eung Kim. (2019) PyOpenDial: A python-based domain-independent toolkit for developing spoken dialogue systems with probabilistic rules. 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): System Demonstrations, pages 187–192.
- Anton Leuski and David Traum. (2011) NPCeditor: Creating virtual human dialogue using information retrieval techniques. Ai Magazine, 32(2):42–56.
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