Abstract
Dialogue systems are an important and very active research area with many practical applications. However, researchers and practitioners new to the field may have difficulty with the categorisation, number and terminology of existing free and commercial systems. Our paper aims to achieve two main objectives. Firstly, based on our structured literature review, we provide a categorisation of dialogue systems according to the objective, modality, domain, architecture, and model, and provide information on the correlations among these categories. Secondly, we summarise and compare frameworks and applications of intelligent virtual assistants, commercial frameworks, research dialogue systems, and large language models according to these categories and provide system recommendations for researchers new to the field.
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docker exec -it bengt/drqa venv/bin/python scripts/pipeline/interactive.py.
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References
Balaraman, V., et al.: Recent neural methods on dialogue state tracking for task-oriented dialogue systems: a survey. In: SIGdial. pp. 239–251. ACL (2021)
Bordes, A., et al.: Learning end-to-end goal-oriented dialog. In: ICLR. OpenReview.net (2017)
Bruss, M., Pfalzgraf, A.: Proaktive assistenzfunktionen für hmis durch künstliche intelligenz. ATZ Automobiltechnische Zeitschrift 118, 42–47 (2016)
Chen, D., et al.: Reading Wikipedia to answer open-domain questions. In: ACL, pp. 1870–1879. ACL (2017)
Chen, H., et al.: A survey on dialogue systems: Recent advances and new frontiers. SIGKDD Explor. 19(2), 25–35 (2017)
Cui, F., et al.: A survey on learning-based approaches for modeling and classification of human-machine dialog systems. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1418–1432 (2021)
Curry, A.C., et al.: A review of evaluation techniques for social dialogue systems. In: SIGCHI, pp. 25–26. ACM (2017)
Deng, L., Liu, Y.: Deep Learning in Natural Language Processing. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5209-5
Deriu, J., et al.: Survey on evaluation methods for dialogue systems. Artif. Intell. Rev. 54(1), 755–810 (2021)
Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171–4186. ACL (2019)
Dinan, E., et al.: The second conversational intelligence challenge (convai2). CoRR abs/1902.00098 (2019)
Driess, D., et al.: PaLM-E: an embodied multimodal language model. CoRR abs/2303.03378 (2023)
Fader, A., et al.: Paraphrase-driven learning for open question answering. In: ACL, pp. 1608–1618. ACL (2013)
Fan, Y., Luo, X.: A survey of dialogue system evaluation. In: 32nd IEEE, ICTAI, pp. 1202–1209. IEEE (2020)
Fan, Y., et al.: MatchZoo: a toolkit for deep text matching. CoRR abs/1707.07270 (2017)
Henderson, J., et al.: Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets. Comput. Linguist. 34(4), 487–511 (2008)
Henderson, M., et al.: The second dialog state tracking challenge. In: SIGDIAL, pp. 263–272 (2014)
Henderson, M., et al.: The third dialog state tracking challenge. In: SLT, pp. 324–329. IEEE (2014)
Hu, J., et al.: SAS: dialogue state tracking via slot attention and slot information sharing. In: ACL, pp. 6366–6375. ACL (2020)
Huang, M., et al.: Challenges in building intelligent open-domain dialog systems. ACM Trans. Inf. Syst. 38(3), 21:1-21:32 (2020)
Huang, P., et al.: Learning deep structured semantic models for web search using clickthrough data. In: ACM, pp. 2333–2338. ACM (2013)
Kim, S., et al.: Efficient dialogue state tracking by selectively overwriting memory. In: ACL, pp. 567–582. ACL (2020)
Koller, A., et al.: DialogOS: simple and extensible dialogue modeling. In: Interspeech, pp. 167–168. ISCA (2018)
Kreyssig, F., et al.: Neural user simulation for corpus-based policy optimisation of spoken dialogue systems. In: SIGdial, pp. 60–69. ACL (2018)
Kuchaiev, O., et al.: Nemo: a toolkit for building AI applications using neural modules. CoRR abs/1909.09577 (2019)
Le, H., et al.: Uniconv: a unified conversational neural architecture for multi-domain task-oriented dialogues. In: EMNLP, pp. 1860–1877. ACL (2020)
Lee, S., et al.: ConvLab: multi-domain end-to-end dialog system platform. In: ACL, pp. 64–69. ACL (2019)
Li, J., et al.: Adversarial learning for neural dialogue generation. In: EMNLP, pp. 2157–2169. ACL (2017)
Li, X., et al.: A review of quality assurance research of dialogue systems. In: AITest, pp. 87–94. IEEE (2022)
Liu, G., et al.: A survey on multimodal dialogue systems: recent advances and new frontiers. In: AEMCSE, pp. 845–853 (2022)
Liu, J., et al.: Review of intent detection methods in the human-machine dialogue system. J. Phys. Conf. Ser. 1267(1), 012059 (2019)
Liu, T.: Learning to rank for information retrieval. In: SIGIR, p. 904. ACM (2010)
Lowe, R., et al.: Towards an automatic turing test: learning to evaluate dialogue responses. In: ACL, pp. 1116–1126. ACL (2017)
Lu, Z., Li, H.: A deep architecture for matching short texts. In: NeurIPS, pp. 1367–1375 (2013)
Ma, L., et al.: Unstructured text enhanced open-domain dialogue system: a systematic survey. ACM Trans. Inf. Syst. 40(1), 9:1-9:44 (2022)
Ma, Y., et al.: A survey on empathetic dialogue systems. Inf. Fus. 64, 50–70 (2020)
Malik, M., et al.: Automatic speech recognition: a survey. Multim. Tools Appl. 80(6), 9411–9457 (2021)
Michael, T.: ReTiCo: an incremental framework for spoken dialogue systems. In: SIGdial, pp. 49–52. ACL (2020)
Michael, T., Möller, S.: ReTiCo: an open-source framework for modeling real-time conversations in spoken dialogue systems. In: ESSV, pp. 134–140 (2019)
Miller, A.H., et al.: ParlAI: a dialog research software platform. In: EMNLP, pp. 79–84. ACL (2017)
Motger, Q., et al.: Software-based dialogue systems: survey, taxonomy, and challenges. ACM Comput. Surv. 55(5), 1–42 (2022)
Nesselrath, R., Feld, M.: SiAM-dp: a platform for the model-based development of context-aware multimodal dialogue applications. In: IE, pp. 162–169. IEEE (2014)
Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. In: NeurIPS, vol. 1773. CEUR-WS.org (2016)
Ni, J., et al.: Recent advances in deep learning based dialogue systems: a systematic survey. CoRR abs/2105.04387 (2021)
Obrenovic, Z., Starcevic, D.: Modeling multimodal human-computer interaction. Computer 37(9), 65–72 (2004)
OpenAI: GPT-4 technical report. CoRR abs/2303.08774 (2023)
Papangelis, A., et al.: Plato dialogue system: a flexible conversational AI research platform. CoRR abs/2001.06463 (2020)
Paul, Z.: Cortana-intelligent personal digital assistant: a review. Int. J. Adv. Res. Comput. Sci. 8, 55–57 (2017)
Rajpurkar, P., et al.: SQuAD: 100,000+ questions for machine comprehension of text. In: EMNLP, pp. 2383–2392. ACL (2016)
Reiter, E.: Has a consensus NL generation architecture appeared, and is it psycholinguistically plausible? In: INLG (1994)
Schatzmann, J., et al.: Agenda-based user simulation for bootstrapping a POMDP dialogue system. In: NAACL HLT, pp. 149–152. ACL (2007)
Seo, M.J., et al.: Real-time open-domain question answering with dense-sparse phrase index. In: ACL, pp. 4430–4441. ACL (2019)
Serban, I.V., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues. In: AAAI, pp. 3295–3301. AAAI Press (2017)
Serban, I.V., et al.: A survey of available corpora for building data-driven dialogue systems: the journal version. Dialogue Discourse 9(1), 1–49 (2018)
Shang, L., et al.: Neural responding machine for short-text conversation. In: ACL, pp. 1577–1586. ACL (2015)
Sonntag, D.: Ontologies and Adaptivity in Dialogue for Question Answering, Studies on the Semantic Web, vol. 4. IOS Press (2010)
Sordoni, A., et al.: A neural network approach to context-sensitive generation of conversational responses. In: NAACL HLT, pp. 196–205. ACL (2015)
Sutskever, I., et al.: Sequence to sequence learning with neural networks. In: NeurIPS, pp. 3104–3112 (2014)
Tan, X., et al.: A survey on neural speech synthesis. CoRR abs/2106.15561 (2021)
Tran, V.-K., Nguyen, L.-M.: Semantic refinement GRU-based neural language generation for spoken dialogue systems. In: Hasida, K., Pa, W.P. (eds.) PACLING 2017. CCIS, vol. 781, pp. 63–75. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8438-6_6
Ultes, S., et al.: PyDial: a multi-domain statistical dialogue system toolkit. In: ACL, pp. 73–78. ACL (2017)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998–6008 (2017)
Vinyals, O., Le, Q.: A neural conversational model. CoRR abs/1506.05869 (2015)
Walker, M.A., et al.: PARADISE: a framework for evaluating spoken dialogue agents. In: ACL, pp. 271–280. ACL (1997)
Wang, S., et al.: R\({}^{\text{3}}\): reinforced ranker-reader for open-domain question answering. In: AAAI, pp. 5981–5988. AAAI Press (2018)
Wang, Y., et al.: Slot attention with value normalization for multi-domain dialogue state tracking. In: EMNLP, pp. 3019–3028. ACL (2020)
Weizenbaum, J.: ELIZA - a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)
Wen, T., et al.: Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking. In: SIGDIAL, pp. 275–284. ACL (2015)
Weston, J., et al.: Retrieve and refine: improved sequence generation models for dialogue. In: SCAI, pp. 87–92. ACL (2018)
Williams, J.D., et al.: The dialog state tracking challenge. In: SIGDIAL, pp. 404–413. ACL (2013)
Wolf, T., et al.: TransferTransfo: a transfer learning approach for neural network based conversational agents. CoRR abs/1901.08149 (2019)
Xu, J., et al.: Diversity-promoting GAN: a cross-entropy based generative adversarial network for diversified text generation. In: EMNLP, pp. 3940–3949. ACL (2018)
Yang, L., et al.: A hybrid retrieval-generation neural conversation model. In: CIKM, pp. 1341–1350. ACM (2019)
Yang, Z., et al.: XLNet: generalized autoregressive pretraining for language understanding. In: NeurIPS, pp. 5754–5764 (2019)
Zhang, Y., et al.: DIALOGPT: large-scale generative pre-training for conversational response generation. In: ACL, pp. 270–278. ACL (2020)
Zhao, T., Eskénazi, M.: Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning. In: SIGDIAL, pp. 1–10. ACL (2016)
Zhao, T., et al.: Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In: ACL, pp. 654–664. ACL (2017)
Zhou, H., et al.: Context-aware natural language generation for spoken dialogue systems. In: COLING, pp. 2032–2041. ACL (2016)
Zhou, L., et al.: The design and implementation of xiaoice, an empathetic social chatbot. Comput. Linguist. 46(1), 53–93 (2020)
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Kath, H., Lüers, B., Gouvêa, T.S., Sonntag, D. (2023). Lost in Dialogue: A Review and Categorisation of Current Dialogue System Approaches and Technical Solutions. In: Seipel, D., Steen, A. (eds) KI 2023: Advances in Artificial Intelligence. KI 2023. Lecture Notes in Computer Science(), vol 14236. Springer, Cham. https://doi.org/10.1007/978-3-031-42608-7_9
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