Abstract
Knowledge-grounded retrieval-based dialogue systems have attracted more and more attention. Among them, the two-stage dialogue models which separate the training stage into knowledge retrieving (via a retriever) and response ranking (via a ranker) are proved powerful. However, these approaches require knowledge-grounded dialogues with corresponding hand-annotated knowledge labels. Therefore, in this paper, we propose training two-stage knowledge-grounded dialogues with knowledge attention feedback from the ranker to the retriever. In each training iteration, the ranker provides knowledge attention scores as pseudo supervised feedback for the optimization of retriever. We conduct experiments on two public data sets. The experimental results demonstrate that our proposed method is superior to the existing baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of Wikipedia: knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241 (2018)
Gu, J.C., Ling, Z.H., Liu, Q., Chen, Z., Zhu, X.: Filtering before iteratively referring for knowledge-grounded response selection in retrieval-based chatbots. arXiv preprint arXiv:2004.14550 (2020)
Gu, J.C., Ling, Z.H., Zhu, X., Liu, Q.: Dually interactive matching network for personalized response selection in retrieval-based chatbots. arXiv preprint arXiv:1908.05859 (2019)
Han, J., Hong, T., Kim, B., Ko, Y., Seo, J.: Fine-grained post-training for improving retrieval-based dialogue systems. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1549–1558 (2021)
Hua, K., Feng, Z., Tao, C., Yan, R., Zhang, L.: Learning to detect relevant contexts and knowledge for response selection in retrieval-based dialogue systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 525–534 (2020)
Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring. arXiv preprint arXiv:1905.01969 (2019)
Izacard, G., Grave, E.: Distilling knowledge from reader to retriever for question answering. arXiv preprint arXiv:2012.04584 (2020)
Ji, Z., Lu, Z., Li, H.: An information retrieval approach to short text conversation. arXiv preprint arXiv:1408.6988 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Lowe, R., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909 (2015)
Mazaré, P.E., Humeau, S., Raison, M., Bordes, A.: Training millions of personalized dialogue agents. arXiv preprint arXiv:1809.01984 (2018)
Miller, A., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. arXiv preprint arXiv:1606.03126 (2016)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Sachan, D.S., et al.: End-to-end training of neural retrievers for open-domain question answering. arXiv preprint arXiv:2101.00408 (2021)
Shum, H.Y., He, X., Li, D.: From Eliza to Xiaoice: challenges and opportunities with social chatbots. Front. Inf. Technol. Electron. Eng. 19(1), 10–26 (2018)
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Tao, C., Chen, C., Feng, J., Wen, J.R., Yan, R.: A pre-training strategy for zero-resource response selection in knowledge-grounded conversations. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4446–4457 (2021)
Tao, C., Feng, J., Liu, C., Li, J., Geng, X., Jiang, D.: Building an efficient and effective retrieval-based dialogue system via mutual learning. arXiv preprint arXiv:2110.00159 (2021)
Tao, C., Wu, W., Xu, C., Hu, W., Zhao, D., Yan, R.: One time of interaction may not be enough: go deep with an interaction-over-interaction network for response selection in dialogues. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1–11 (2019)
Turc, I., Chang, M.W., Lee, K., Toutanova, K.: Well-read students learn better: on the importance of pre-training compact models. arXiv preprint arXiv:1908.08962 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vig, J., Ramea, K.: Comparison of transfer-learning approaches for response selection in multi-turn conversations. In: Workshop on DSTC7 (2019)
Wang, H., Lu, Z., Li, H., Chen, E.: A dataset for research on short-text conversations. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 935–945 (2013)
Wang, M., Lu, Z., Li, H., Liu, Q.: Syntax-based deep matching of short texts. In: 24th International Joint Conference on Artificial Intelligence (2015)
Wu, L., Fisch, A., Chopra, S., Adams, K., Bordes, A., Weston, J.: Starspace: embed all the things! In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential matching network: a new architecture for multi-turn response selection in retrieval-based chatbots. arXiv preprint arXiv:1612.01627 (2016)
Zhang, C., Wang, H., Jiang, F., Yin, H.: Adapting to context-aware knowledge in natural conversation for multi-turn response selection. In: Proceedings of the Web Conference 2021, pp. 1990–2001 (2021)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: i have a dog, do you have pets too? arXiv preprint arXiv:1801.07243 (2018)
Zhao, X., Tao, C., Wu, W., Xu, C., Zhao, D., Yan, R.: A document-grounded matching network for response selection in retrieval-based chatbots. arXiv preprint arXiv:1906.04362 (2019)
Zhou, K., Prabhumoye, S., Black, A.W.: A dataset for document grounded conversations. arXiv preprint arXiv:1809.07358 (2018)
Zhou, X., et al.: Multi-view response selection for human-computer conversation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 372–381 (2016)
Zhou, X., et al.: Multi-turn response selection for chatbots with deep attention matching network. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1118–1127 (2018)
Acknowledgments
This work is supported in part by NSFC (No. 2021YFC3340304). We would like to thank the anonymous reviewers and action editors for their helpful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Z., Feng, J., Tao, C., Zhao, D. (2022). Training Two-Stage Knowledge-Grounded Dialogues with Attention Feedback. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https://doi.org/10.1007/978-3-031-17120-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-17120-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17119-2
Online ISBN: 978-3-031-17120-8
eBook Packages: Computer ScienceComputer Science (R0)
