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Enhancing Self-disclosure In Open-Domain Dialogue By Candidate Re-ranking

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Conversational AI for Natural Human-Centric Interaction

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 943))

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Abstract

Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialog models have been criticised for generating responses which although may have relevance to the previous human response, tend to quickly dissipate human interest and descend into trivial conversation. One reason for such performance is the lack of explicit conversation strategy being employed in human-machine conversation. Humans employ a range of conversation strategies while engaging in a conversation, one such key social strategies is Self-disclosure (SD). A phenomenon of revealing information about one-self to others. In this work, Self-disclosure enhancement architecture (SDEA) is introduced utilizing Self-disclosure Topic Model (SDTM) during inference stage of a neural dialog model to re-rank response candidates to enhance self-disclosure in single-turn responses from the model.

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References

  1. Joinson AN, Paine CB (2007) Self-disclosure, privacy and the internet. The Oxford handbook of internet psychology, 2374252

    Google Scholar 

  2. Altman I, Taylor DA (1973): Social penetration: the development of interpersonal relationships. Holt, New York

    Google Scholar 

  3. Bak A (2014) Self-disclosure topic model for classifying and analyzing Twitter conversations. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, pp 1986–1996

    Google Scholar 

  4. Ravichander A, Black AW (2018) An empirical study of self-disclosure in spoken dialogue systems. In: Proceedings of the 19th annual SIGdial meeting on discourse and dialogue. Association for Computational Linguistics, pp 253–263

    Google Scholar 

  5. Ravichander A (2018) An empirical study of self-disclosure in spoken dialogue systems. In: Proceedings of the 19th annual SIGdial meeting on discourse and dialogue. Association for Computational Linguistics, pp 253–263

    Google Scholar 

  6. Vondracek S, Vondracek F (1971) The manipulation and measurement of self-disclosure in preadolescents. Merrill-Palmer Q Behav Devel 17(1):51–58

    Google Scholar 

  7. Barak A, Gluck-Ofri O (2007) Degree and reciprocity of self-disclosure in online forums. Cyberpsychol Behav 10(3):407–417

    Article  Google Scholar 

  8. Yang D, Yao Z, Kraut R (2017) Self-disclosure and channel difference in online health support groups. In: Proceedings of the international AAAI conference on web and social media, vol 11, No 1

    Google Scholar 

  9. Zhang Y, Sun S, Galley M, Chen YC, Brockett C, Gao X, ... Dolan B (2019). Dialogpt: large-scale generative pre-training for conversational response generation. arXiv:1911.00536

  10. Roller S, Dinan E, Goyal N, Ju D, Williamson M, Liu Y, Xu J, Ott M, Shuster K, Smith EM, Boureau Y-L, Weston J (2020) Recipes for building an open-domain chatbot

    Google Scholar 

  11. Fan Y (2018) Hierarchical neural story generation. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: long papers). Association for Computational Linguistics, pp 889–898

    Google Scholar 

  12. Vinyals O, Le Q (2015) A neural conversational model

    Google Scholar 

  13. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9

    Google Scholar 

  14. Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training

    Google Scholar 

  15. andreamad8. (n.d.) andreamad8/DialoGPT2-Interact. GitHub. https://github.com/andreamad8/DialoGPT2-Interact

  16. Holtzman A, Buys J, Du L, Forbes M, Choi Y (2020) The curious case of neural text degeneration

    Google Scholar 

  17. Spencer-Oatey H (2008) Culturally speaking second edition: culture, communication and politeness theory. Bloomsbury Publishing

    Google Scholar 

  18. Tracy K, Coupland N (1990) Multiple goals in discourse: an overview of issues. J Lang Soc Psychol 9(1–2):1–13

    Article  Google Scholar 

  19. Jain A, Pecune F, Matsuyama Y, Cassell J (2018) A user simulator architecture for socially-aware conversational agents. In: Proceedings of the 18th international conference on intelligent virtual agents, pp 133–140

    Google Scholar 

  20. Adiwardana D, Luong MT, So D, Hall J, Fiedel N, Thoppilan R, Yang Z, Kulshreshtha A, Nemade G, Lu Y, others (2020) Towards a human-like open-domain chatbot. arXiv:2001.09977

  21. Huang M, Zhu X, Gao J (2020) Challenges in building intelligent open-domain dialog systems. ACM Trans Inf Syst (TOIS) 38(3):1–32

    Google Scholar 

  22. Gao J, Galley M, Li L (2018) Neural approaches to conversational ai. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 1371–1374

    Google Scholar 

  23. Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J (2018) Personalizing dialogue agents: i have a dog, do you have pets too? arXiv:1801.07243

  24. Yanran L, Su H, Xiaoyu S, Wenjie L, Ziqiang C, Niu S (2017) DailyDialog: a manually labelled multi-turn dialogue dataset

    Google Scholar 

  25. Calhoun S, Carletta J, Brenier JM, Mayo N, Jurafsky D, Steedman M, Beaver D (2010) The NXT-format Switchboard Corpus: a rich resource for investigating the syntax, semantics, pragmatics and prosody of dialogue. Lang Resour Eval 44(4):387–419

    Article  Google Scholar 

  26. Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311–318

    Google Scholar 

  27. Doddington G (2002) Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In: Proceedings of the second international conference on human language technology research, pp 138–145

    Google Scholar 

  28. Lavie A, Agarwal A (2007) METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments. In: Proceedings of the second workshop on statistical machine translation, pp. 228–231

    Google Scholar 

  29. Zhang Y, Galley M, Gao J, Gan Z, Li X, Brockett C, Dolan B (2018) Generating informative and diverse conversational responses via adversarial information maximization. arXiv:1809.05972

  30. Li J, Galley M, Brockett C, Gao J, Dolan B (2015) A diversity-promoting objective function for neural conversation models. arXiv:1510.03055

  31. Gupta P, Mehri S, Zhao T, Pavel A, Eskenazi M, Bigham JP (2019) Investigating evaluation of open-domain dialogue systems with human generated multiple references. arXiv:1907.10568

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Acknowledgements

This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (D-REAL) under Grant No. 18/CRT/6224. We would like to thank anonymous reviewers from IWSDS 2021 for their valuable comments.

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Correspondence to Mayank Soni .

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Soni, M., Cowan, B.R., Wade, V. (2022). Enhancing Self-disclosure In Open-Domain Dialogue By Candidate Re-ranking. In: Stoyanchev, S., Ultes, S., Li, H. (eds) Conversational AI for Natural Human-Centric Interaction. Lecture Notes in Electrical Engineering, vol 943. Springer, Singapore. https://doi.org/10.1007/978-981-19-5538-9_17

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  • DOI: https://doi.org/10.1007/978-981-19-5538-9_17

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  • Online ISBN: 978-981-19-5538-9

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