The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Abstract
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data.The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
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BLEU: a method for automatic evaluation of machine translation
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ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
SWITCHBOARD: telephone speech corpus for research and development
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“Switchboard [7] Human-human Various 2,400 — 3,000,000 Telephone conversations spoken on pre-specified topics DSTC1 [30] Human-computer State 15,000 210,000 Bus ride information spoken tracking system DSTC2 [9] Human-computer State 3,000 24,000 — Restaurant booking spoken tracking system DSTC3 [8] Human-computer State…”
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Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., & Gašić, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1547 · Cited by 1,028
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Chen, H., Liu, X., Yin, D., & Tang, J. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3166054.3166058 · Cited by 579
Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.121 · Cited by 445
Metadata
- Authors
- Lowe, R., Pow, N., Serban, I., & Pineau, J.
- Venue
- Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Published
- 2015
- Pages
- 285-294
- OpenAlex
- W2962854379
- Cited by
- 748
- References
- 6
- FWCI
- 44.42
- Type
- journal article
- Language
- en
- Open access
- closed
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