The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Recipes for Building an Open-Domain Chatbot
Open accessAbstract
Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Given the rapid advances in conversational AI 79 , coupled with the development of increasingly sophisticated multimodal learning approaches, we expect future digital health applications to embrace the potential of AI to deliver accurate and personalized health coaching.”
From Multimodal biomedical AI (2022) · cites this paper as [79]
In the citing paper
“along with high-quality open-source libraries [113] have lowered the barrier to entry and make it possible to build conversational models that exhibit high generalization and coherence [90].”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [90]
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (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/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Self-citationScaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×
“The larger model achieves a better performance in fewer steps, consistent with other works (Kaplan et al., 2020; Li et al., 2020).”
ResultLewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Self-citationLewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816
Self-citationLongformer: The Long-Document Transformer
Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281
Transformer-XL: Attentive Language Models Beyond a Fixed-Length ContextPreprint
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., & Salakhutdinov, R. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.ecrts.2023.7 · Cited by 1,476
Reformer: The Efficient Transformer
Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020) · arXiv · doi:10.48550/arxiv.2001.04451 · Cited by 1,435
Adafactor: Adaptive Learning Rates with Sublinear Memory CostPreprint
Shazeer, N., & Stern, M. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1804.04235 · Cited by 1,410
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242
The Pushshift Reddit DatasetPreprint
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · International Conference on Web and Social Media · doi:10.48550/arxiv.2001.08435 · Cited by 1,185
A Deep Reinforced Model for Abstractive Summarization
Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al. (2018) · arXiv · doi:10.48550/arxiv.1811.06965 · Cited by 1,180
Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al. (2017) · arXiv · doi:10.48550/arxiv.1710.03740 · Cited by 1,179
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
Self-citationMegatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · arXiv · doi:10.48550/arxiv.1909.08053 · Cited by 893
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7347 · Cited by 881
The design and implementation of Xiaoice, an empathetic social chatbot
Zhou, L., Gao, J., Li, D., & Shum, H.-Y. (2020) · Computational Linguistics · doi:10.1162/coli_a_00368 · Cited by 777
Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01241 · Cited by 755
Self-citationCTRL: A Conditional Transformer Language Model for Controllable Generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., & Socher, R. (2019) · arXiv · doi:10.48550/arxiv.1909.05858 · Cited by 751
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749
Towards a Human-like Open-Domain Chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational AgentsPreprint
Wolf, T., Sanh, V., Chaumond, J., & Delangue, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08149 · Cited by 495
fairseq: A Fast, Extensible Toolkit for Sequence ModelingPreprint
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01038 · Cited by 471
Self-citation(2020) · arXiv (Cornell University) · Cited by 461
Bisk, Y., Holtzman, A., Thomason, J., Andreas, J., Bengio, Y., Chai, J. Y., Lapata, M., Lazaridou, A., May, J., Nisnevich, A., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.703 · Cited by 421
The Second Conversational Intelligence Challenge (ConvAI2)
Dinan, E., Logacheva, V., Malykh, V., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A. D., Serban, I. V., Lowe, R., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_7 · Cited by 395
Self-citationParlAI: A Dialog Research Software Platform
Miller, A., Feng, W., Batra, D., Bordes, A., Fisch, A., Lu, J., Parikh, D., & Weston, J. (2017) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d17-2014 · Cited by 386
Self-citationDialoGPT: Large-Scale Generative Pre-training for Conversational Response GenerationPreprint
Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00536 · Cited by 258
Training Millions of Personalized Dialogue Agents
Mazaré, P.-E., Humeau, S., Raison, M., & Bordes, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1298 · Cited by 226
Building a Conversational Agent Overnight with Dialogue Self-PlayPreprint
Shah, P., Hakkani-Tür, D. Z., Tür, G., Rastogi, A., Bapna, A., Nayak, N., & Heck, L. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04871 · Cited by 211
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills
Smith, E. M., Williamson, M., Shuster, K., Weston, J., & Boureau, Y.-L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.183 · Cited by 207
Self-citationLearning to Speak and Act in a Fantasy Text Adventure Game
Urbanek, J., Fan, A., Karamcheti, S., Jain, S., Humeau, S., Dinan, E., Rocktäschel, T., Kiela, D., Szlam, A. D., & Weston, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1062 · Cited by 195
Self-citationLearning to Write with Cooperative Discriminators
Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1152 · Cited by 192
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Dinan, E., Fan, A., Williams, A., Urbanek, J., Kiela, D., & Weston, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.656 · Cited by 190
Self-citationHumeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 177
Self-citationLearning Semantic Textual Similarity from Conversations
Yang, Y., Yuan, S., Cer, D. M., Kong, S.-Y., Constant, N., Pilar, P., Ge, H., Sung, Y.-H., Strope, B. P., & Kurzweil, R. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3022 · Cited by 172
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Weston, J., Dinan, E., & Miller, A. (2018) · Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI · doi:10.18653/v1/w18-5713 · Cited by 165
Self-citationLearning from Dialogue after Deployment: Feed Yourself, Chatbot!
Hancock, B., Bordes, A., Mazare, P.-E., & Weston, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1358 · Cited by 161
Self-citationBuild it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
Dinan, E., Humeau, S., Chintagunta, B., & Weston, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1461 · Cited by 161
Self-citationDon’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Li, M., Roller, S., Kulikov, I., Welleck, S., Boureau, Y.-L., Cho, K., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.428 · Cited by 151
Self-citationA Persona-Based Neural Conversation ModelPreprint
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06155 · Cited by 148
ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn ComparisonsPreprint
Li, M., Weston, J., & Roller, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03087 · Cited by 139
Self-citationTrain Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Li, Z., Wallace, E., Shen, S., Lin, K., Keutzer, K., Klein, D., & Gonzalez, J. E. (2020) · International Conference on Machine Learning · Cited by 125
Shah, P., Hakkani-Tür, D. Z., Liu, B., & Tür, G. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-3006 · Cited by 118
Li, Z., Wallace, E., Shen, S., Lin, K., Keutzer, K., Klein, D., & Gonzalez, J. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11794 · Cited by 107
Neural Text Generation With Unlikelihood Training
Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 106
Self-citationDialog-based Language LearningPreprint
Weston, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06045 · Cited by 88
Self-citationThe Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
Shuster, K., Da, J. U., Roller, S., Dinan, E., Boureau, Y.-L., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.222 · Cited by 78
Self-citationApproximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog SystemsPreprint
Ghandeharioun, A., Shen, J. H., Jaques, N., Ferguson, C., Jones, N. C., Lapedriza, À., & Picard, R. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09308 · Cited by 77
How Decoding Strategies Affect the Verifiability of Generated Text
Massarelli, L., Petroni, F., Piktus, A., Ott, M., Rocktäschel, T., Plachouras, V., Silvestri, F., & Riedel, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.22 · Cited by 44
Self-citationOpen-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
Self-citationLearning Robust Dialog Policies in Noisy EnvironmentsPreprint
Fazel-Zarandi, M., Li, S.-W. D., Cao, J. L., Casale, J., Henderson, P., Whitney, D., & Geramifard, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04034 · Cited by 20
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood TrainingPreprint
Li, M., Roller, S., Kulikov, I., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03860 · Cited by 19
Self-citationAnalyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response ModelsPreprint
He, T., Liu, J., Cho, K., Ott, M., Liu, B., Glass, J. R., & Peng, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07117 · Cited by 19
Self-citationPrabhumoye, S., Li, M., Urbanek, J., Dinan, E., Kiela, D., Weston, J., & Szlam, A. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02878 · Cited by 11
Self-citationSequence Effects in Crowdsourced Annotations
Mathur, N., Baldwin, T. J., & Cohn, T. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1306 · Cited by 10
I love your chain mail! Making knights smile in a fantasy game world
Prabhumoye, S., Li, M., Urbanek, J., Dinan, E., Kiela, D., Weston, J., & Szlam, A. D. (2019) · Cited by 2
Self-citationA Goal-oriented Neural Conversation Model by Self-Play
Wei, W., Le, Q. V., Dai, A. M., & Li, L.-J. (2018) · Cited by 1
Bisk, Y., Holtzman, A., Thomason, J., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.10151
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Dinan, E., Fan, A., Williams, A., et al. (2019) · arXiv · doi:10.48550/arxiv.1911.03842
Self-citationHow Decoding Strategies Affect the Verifiability of Generated Text
Massarelli, L., Petroni, F., Piktus, A., Ott, M., Rocktäschel, T., Plachouras, V., Silvestri, F., & Riedel, S. (2019) · arXiv · doi:10.48550/arxiv.1911.03587
Self-citation
Research works printed but not linked: 6
- Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019c. Wiz- ard of Wikipedia: Knowledge-powered conversa- tional agents. In Proceedings of the International Conference on Learning Representations.
- Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hi- erarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889-898.
- ACL.
- Jack W. Rae, Anna Potapenko, Siddhant M. Jayaku- mar, Chloe Hillier, and Timothy P. Lillicrap. 2020. Compressive transformers for long-range sequence modelling. In International Conference on Learn- ing Representations.
- Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open- domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguis- tics, pages 5370-5381, Florence, Italy. Association for Computational Linguistics.
- Jason E Weston. 2016. Dialog-based language learn- ing. In Advances in Neural Information Processing Systems, pages 829-837.
Cited by
Sorted by citations
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994
Self-citationThe Rise and Potential of Large Language Model Based Agents: A Survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970
Metadata
- Authors
- Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., & Weston, J.
- Affiliations
- Meta (United States) (US)
- Venue
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Published
- 2021
- Pages
- 300-325
- OpenAlex
- W3023786569
- Cited by
- 784
- References
- 57
- Citations by year
- 2026: 42 · 2025: 76 · 2024: 88 · 2023: 206 · 2022: 183 · 2021: 141
- FWCI
- 71.39
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the European Chapter of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- open-domain chatbots, chatbot development, conversational AI, dialogue systems, chatbot evaluation
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