Abstract
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
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References
Allen JF, Perrault CR (1980) Analyzing intention in utterances. Artif Intell 15(3):143–178
Bonial C, Marge M, Foots A, Gervits F, Hayes CJ, Henry C, Hill SG, Leuski A, Lukin SM, Moolchandani P, Pollard KA, Traum D, Voss CR (2017) Laying down the yellow brick road: development of a wizard-of-oz interface for collecting human-robot dialogue. In: Symposium on natural communication for human-robot collaboration, AAAI FSS
Bugmann G, Klein E, Lauria S, Kyriacou T (2004) Corpus-based robotics: a route instruction example. In: Proceedings of intelligent autonomous systems, pp 96–103
Cohen PR, Perrault CR (1979) Elements of a plan-based theory of speech acts. Cogn Sci 3(3):177–212
Dahlbäck N, Jönsson A, Ahrenberg L (1993) Wizard of oz studies - why and how. Knowl-Based Syst 6(4):258–266
Eberhard KM, Nicholson H, Kübler S, Gundersen S, Scheutz M (2010) The indiana “cooperative remote search task” (crest) corpus. In: Proceedings of LREC 2010
Gervits F, Eberhard K, Scheutz M (2016) Team communication as a collaborative process. Front Robot AI 3:62
Issar S, Ward W (1993) CMLPs robust spoken language understanding system. In: Third European conference on speech communication and technology
Leuski A, Traum D (2008) A statistical approach for text processing in virtual humans. In: Proceedings of the 26th army science conference
Leuski A, Traum D (2011) NPC editor: creating virtual human dialogue using information retrieval techniques. AI Mag 32(2):42–56
Leuski A, Traum DR (2010) Practical language processing for virtual humans. In: IAAI-10
Lukin SM, Gervits F, Hayes CJ, Moolchandani P, Leuski A, Rogers III JG, Amaro CS, Marge M, Voss CR, Traum D (2018) ScoutBot: a dialogue system for collaborative navigation. In: Proceedings of ACL
Marge M, Bonial C, Byrne B, Cassidy T, Evans AW, Hill SG, Voss C (2016) Applying the wizard-of-oz technique to multimodal human-robot dialogue. In: Proceedings of RO-MAN
Marge M, Bonial C, Pollard KA, Artstein R, Byrne B, Hill SG, Voss C, Traum D (2016) Assessing agreement in human-robot dialogue strategies: a tale of two wizards. In: International conference on intelligent virtual agents. Springer, pp 484–488
Marge M, Bonial C, Foots A, Hayes C, Henry C, Pollard K, Artstein R, Voss C, Traum D (2017) Exploring variation of natural human commands to a robot in a collaborative navigation task. In: Proceedings of the first workshop on language grounding for robotics, pp 58–66
Marge M, Bonial C, Lukin S, Hayes C, Foots A, Artstein R, Henry C, Pollard K, Gordon C, Gervits F, Leuski A, Hill S, Voss C, Traum D (2018) Balancing efficiency and coverage in human-robot dialogue collection. In: Proceedings of AI-HRI AAAI-FSS
Marge MR, Rudnicky AI (2011) The teamtalk corpus: route instructions in open spaces. In: Proceedings of SIGdial
McTear MF (1998) Modelling spoken dialogues with state transition diagrams: experiences with the CSLU toolkit. In: Fifth international conference on spoken language processing
Murphy R (2004) Human-robot interaction in rescue robotics. IEEE Trans Syst Man Cybern Part C 34(2):138–153
Serban IV, Sordoni A, Bengio Y, Courville AC, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: AAAI, vol 16, pp 3776–3784
Serban IV, Lowe R, Henderson P, Charlin L, Pineau J (2018) A survey of available corpora for building data-driven dialogue systems: the journal version. Dialogue Discourse 9(1):1–49
Traum D, Georgila K, Artstein R, Leuski A (2015) Evaluating spoken dialogue processing for time-offset interaction. In: Proceedings of SIGdial, pp 199–208
Traum DR, Henry C, Lukin SM, Artstein R, Pollard KA, Bonial C, Lei S, Voss CR, Marge M, Hayes C, Hill S (2018) Dialogue structure annotation for multi-floor interaction. In: Proceedings of LREC
Vinyals O, Le Q (2015) A neural conversational model. arXiv:1506.05869
Wen TH, Gasic M, Mrksic N, Su PH, Vandyke D, Young S (2015) Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. In: Proceedings of EMNLP
Xu W, Rudnicky AI (2000) Task-based dialog management using an agenda. In: Proceedings of the 2000 ANLP/NAACL workshop on conversational systems, vol 3, pp 42–47
Acknowledgements
This research was sponsored by the U.S. Army Research Laboratory and by a NASA Space Technology Research Fellowship for the first author.
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Gervits, F., Leuski, A., Bonial, C., Gordon, C., Traum, D. (2021). A Classification-Based Approach to Automating Human-Robot Dialogue. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_10
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