Generating personalized responses is one of the major challenges in natural human–robot interaction. Current studies in this field mainly focus on generating responses consistent with the robot’s pre-assigned persona, while ignoring the user’s persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a bilateral personalized dialogue generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the conditional mutual information maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the proposed method achieves the improvement on the random personalized test set of 23.99 in bilateral persona accuracy, 1.1 in BLEU, 0.83 in F1, 0.02 in distinct score, and the improvement on the biased personalized test set of 5.56 in bilateral persona accuracy, 7.51 in BLEU, 2.12 in F1, 0.02 in distinct score. On the manual evaluations, the proposed method can generate more fluency, bilateral persona-consistent, and context-consistent responses compared with other state-of-the-art methods.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Enquiries about data availability should be directed to the authors.
Adiwardana D, Luong, et al (2020) Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977
Baltescu P, Blunsom P (2015) Pragmatic neural language modelling in machine translation. In: Proceedings of the 2015 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 820–829
Cai H, Chen H, Song Y, Ding Z, Bao Y, Yan W, Zhao X (2020) Group-wise contrastive learning for neural dialogue generation. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, pp 793–802
Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, PMLR, pp 1597–1607
Dai B, Lin D (2017) Contrastive learning for image captioning. In: Proceedings of the 31st international conference on neural information processing systems, pp 898–907
Dash PB, Naik B, Nayak J, Vimal S (2021) Deep belief network-based probabilistic generative model for detection of robotic manipulator failure execution. Soft Comput pp 1–13
Dinan E, Logacheva V, Malykh V, Miller A, Shuster K, Urbanek J, Kiela D, Szlam A, Serban I, Lowe R, et al (2019) The second conversational intelligence challenge (convai2). arXiv preprint arXiv:1902.00098
Fleuret F (2004) Fast binary feature selection with conditional mutual information. J Mach Learn Res 5:1531–1555
Goldberg LR (1993) The structure of phenotypic personality traits. Am Psychol 48(1):26–34
Golovanov S, Kurbanov R, Nikolenko S, Truskovskyi K, Tselousov A, Wolf T (2019) Large-scale transfer learning for natural language generation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 6053–6058
Golovanov S, Tselousov A, Kurbanov R, Nikolenko SI (2020) Lost in conversation: A conversational agent based on the transformer and transfer learning. In: The NeurIPS’18 competition, Springer, pp 295–315
Gutmann MU, Hyvärinen A (2012) Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J Mach Learn Res 13(2)
Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), IEEE, vol 2, pp 1735–1742
He D, Xia Y, Qin T, Wang L, Yu N, Liu TY, Ma WY (2016) Dual learning for machine translation. In: Advances in neural information processing systems, pp 820–828
Huang F, Wan D, Shao Z, Ke P, Guan J, Niu Y, Zhu X, Huang M (2020a) Cotk: An open-source toolkit for fast development and fair evaluation of text generation. arXiv preprint arXiv:2002.00583
Huang M, Zhu X, Gao J (2020) Challenges in building intelligent open-domain dialog systems. ACM Trans Inf Syst (TOIS) 38(3):1–32
Isard A, Brockmann C, Oberlander J (2006) Individuality and alignment in generated dialogues. In: Proceedings of the fourth international natural language generation conference, pp 25–32
Kulikov I, Lee J, Cho K (2019) Multi-turn beam search for neural dialogue modeling. arXiv preprint arXiv:1906.00141
Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: A lite bert for self-supervised learning of language representations. In: International conference on learning representations
Li J, Galley M, Brockett C, Gao J, Dolan B (2016a) A diversity-promoting objective function for neural conversation models. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 110–119
Li J, Galley M, Brockett C, Spithourakis G, Gao J, Dolan B (2016b) A persona-based neural conversation model. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 994–1003
Liu Y, Liu P (2021) Simcls: A simple framework for contrastive learning of abstractive summarization. arXiv preprint arXiv:2106.01890
Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1412–1421
Ma WJLHH et al (2021) Hierarchical matching network for multi-turn response selection in retrieval-based chatbots. Soft Comput 9:9609–9624
Madotto A, Lin Z, Wu CS, Fung P (2019) Personalizing dialogue agents via meta-learning. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5454–5459
Mairesse F, Walker M (2007) Personage: Personality generation for dialogue. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 496–503
Martin TP, Azvine B (2003) Adaptive user modelling in intelligent telephone and email assistants. Soft Comput 8(2):93–101
Mo K, Li S, Zhang Y, Li J, Yang Q (2016) Personalizing a dialogue system with transfer reinforcement learning. arXiv preprint arXiv:1610.02891
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
Qian Q, Huang M, Zhao H, Xu J, Zhu X (2018) Assigning personality/profile to a chatting machine for coherent conversation generation. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 4279–4285
Randolph JJ (2005) Free-marginal multirater kappa (multirater k [free]): An alternative to fleiss’ fixed-marginal multirater kappa. Online submission
Roller S, Boureau YL, Weston J, Bordes A, Dinan E, Fan A, Gunning D, Ju D, Li M, Poff S, et al. (2020) Open-domain conversational agents: Current progress, open problems, and future directions. arXiv preprint arXiv:2006.12442
Rush AM (2018) The annotated transformer. In: Proceedings of workshop for NLP open source software (NLP-OSS), pp 52–60
Song H, Zhang WN, Cui Y, Wang D, Liu T (2019) Exploiting persona information for diverse generation of conversational responses. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI Press, pp 5190–5196
Sun Y, Wang S, Li Y, Feng S, Chen X, Zhang H, Tian X, Zhu D, Tian H, Wu H (2019) Ernie: Enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112
Tanaka T, Ohwi J, Litvintseva LV, Yamafuji K, Ulyanov SV (1997) Soft computing algorithms for intelligent control of a mobile robot for service use. Soft Comput 1(2):88–98
Tramontano A, Scala M, Magliulo M (2019) Wearable devices for health-related quality of life evaluation. Soft Comput 23(19):9315–9326
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008
Vijayakumar AK, Cogswell M, Selvaraju RR, Sun Q, Lee S, Crandall D, Batra D (2016) Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424
Walker MA, Cahn JE, Whittaker SJ (1997) Improvising linguistic style: Social and affective bases for agent personality. In: Proceedings of 1st international conference autonomation agents, pp 96–105
Wang D, Zheng TF (2015) Transfer learning for speech and language processing. In: 2015 Asia-Pacific signal and information processing association annual summit and conference (APSIPA), IEEE, pp 1225–1237
Wang Y, Ke P, Zheng Y, Huang K, Jiang Y, Zhu X, Huang M (2020) A large-scale chinese short-text conversation dataset. arXiv preprint arXiv:2008.03946
Wolf T, Sanh V, Chaumond J, Delangue C (2019) Transfertransfo: A transfer learning approach for neural network based conversational agents. arXiv preprint arXiv:1901.08149
Xu M, Li P, Yang H, Ren P, Ren Z, Chen Z, Ma J (2020) A neural topical expansion framework for unstructured persona-oriented dialogue generation. arXiv preprint arXiv:2002.02153
Yang M, Huang W, Tu W, Qu Q, Shen Y, Lei K (2020) Multitask learning and reinforcement learning for personalized dialog generation: An empirical study. IEEE transactions on neural networks and learning systems pp 1–14, 10.1109/TNNLS.2020.2975035
Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J (2018a) Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 2204–2213
Zhang WN, Zhu Q, Wang Y, Zhao Y, Liu T (2018) Neural personalized response generation as domain adaptation. World Wide Web 22(4):1427–1446
Zheng Y, Chen G, Huang M, Liu S, Zhu X (2019) Personalized dialogue generation with diversified traits. arXiv preprint arXiv:1901.09672
Zheng Y, Zhang R, Huang M, Mao X (2020) A pre-training based personalized dialogue generation model with persona-sparse data. AAAI Press, pp 9693–9700
The authors have not disclosed any funding.
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, B., Deng, H. Bilateral personalized dialogue generation with contrastive learning. Soft Comput 27, 3115–3132 (2023). https://doi.org/10.1007/s00500-022-07495-w
- Bilateral persona-consistent
- Conditional mutual information maximum
- Contrastive learning
- Personalized dialogue generation