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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
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
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134 · cited 2×
“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…”
BackgroundMethod
Cited by
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
Attention-Based Multimodal Fusion for Video Description
Hori, C., Hori, T., Lee, T.-Y., Zhang, Z., Harsham, B., Hershey, J. R., Marks, T. K., & Sumi, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.450 · Cited by 401
Challenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288
What makes a good conversation? How controllable attributes affect human judgments
See, A., Roller, S., Kiela, D., & Weston, J. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1170 · Cited by 247
A Survey of Evaluation Metrics Used for NLG Systems
Sai, A. B., Mohankumar, A. K., & Khapra, M. M. (2023) · ACM Computing Surveys · doi:10.1145/3485766 · Cited by 235
Information Leakage in Embedding Models
Song, C., & Raghunathan, A. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417270 · Cited by 175
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) · arXiv · doi:10.48550/arxiv.1801.07243 · Cited by 165
Natural language does not emerge ‘naturally’ in multi-agent dialog
Kottur, S., Moura, J., Lee, S., & Batra, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1321 · Cited by 164
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
Mehri, S., & Eskenazi, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.64 · Cited by 149
Generation-Augmented Retrieval for Open-Domain Question Answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2021) · 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) · doi:10.18653/v1/2021.acl-long.316 · Cited by 146
MuTual: A Dataset for Multi-Turn Dialogue Reasoning
Cui, L., Wu, Y., Liu, S., Zhang, Y., & Zhou, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.130 · Cited by 110
Auditing Data Provenance in Text-Generation Models
Song, C., & Shmatikov, V. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330885 · Cited by 103
Conversational Query Understanding Using Sequence to Sequence Modeling
Ren, G., Ni, X., Malik, M., & Ke, Q. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186083 · Cited by 55
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.01672 · Cited by 49
Hashemi, H., Eisner, J., Rosset, C., Van Durme, B., & Kedzie, C. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.745 · Cited by 47
Learning towards conversational AI: A survey
Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46
Personalized chatbot trustworthiness ratings
Srivastava, B., Rossi, F., Usmani, S., & Bernagozzi, M. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.3023919 · Cited by 40
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38
Generation-Augmented Retrieval for Open-domain Question Answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2020) · arXiv · doi:10.48550/arxiv.2009.08553 · Cited by 31
Yi, S., Goel, R., Khatri, C., Cervone, A., Chung, T., Hedayatnia, B., Venkatesh, A., Gabriel, R., & Hakkani-Tur, D. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8608 · Cited by 26
Large scale multi-actor generative dialog modeling,
Boyd, A., Puri, R., Shoeybi, M., Patwary, M., & Catanzaro, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.8 · Cited by 22
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Wang, X., Zhang, H., Zhao, S., Zou, Y., Chen, H., Ding, Z., Cheng, B., & Lan, Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.362 · Cited by 4
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
