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Interpretation of Natural Language Rules in Conversational Machine Reading
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Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
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“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [115]
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Ishii, E., Wilie, B., Xu, Y., Cahyawijaya, S., & Fung, P. (2022) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2022.acl-srw.6 · Cited by 6
Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents
Du, H., Feng, Y., Li, C., Yang, L., Lan, Y., & Zhao, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.391 · Cited by 6
Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Wan, H., Feng, S., Gunasekara, C., Patel, S. S., Joshi, S., & Lastras, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.367 · Cited by 6
CookDial: a dataset for task-oriented dialogs grounded in procedural documents
Jiang, Y., Zaporojets, K., Deleu, J., Demeester, T., & Develder, C. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03692-0 · Cited by 7
Evaluating Rewards for Question Generation ModelsPreprint
Hosking, T., & Riedel, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11049 · Cited by 6
Self-citationDiscern: Discourse-Aware Entailment Reasoning Network for Conversational Machine ReadingPreprint
Gao, Y., Wu, C.-S., Li, J., Joty, S., Hoi, S. C. H., Xiong, C., King, I., & Lyu, M. R.-T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01838 · Cited by 6
Li, K., Zhang, T., Tang, L., Li, J., Lu, H., Wu, X., & Meng, H. M. L. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.13 · Cited by 5
“What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge
Zhao, C., Gella, S., Kim, S., Jin, D., Hazarika, D., Papangelis, A., Hedayatnia, B., Namazifar, M., Liu, Y., & Hakkani-Tür, D. Z. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.28 · Cited by 8
TITAN : Task-oriented Dialogues with Mixed-Initiative Interactions
Yan, S., Song, S., Li, J., Meng, S., & Hu, G. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/583 · Cited by 5
Natural Language QA Approaches using Reasoning with External KnowledgePreprint
Baral, C. R., Banerjee, P., Pal, K. K., & Mitra, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03446 · Cited by 5
Iterative Hierarchical Attention for Answering Complex Questions over Long DocumentsPreprint
Sun, H., Cohen, W. W., & Salakhutdinov, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00200 · Cited by 15
DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling
Feng, S. (2021) · doi:10.18653/v1/2021.dialdoc-1.1 · Cited by 4
Smoothing Dialogue States for Open Conversational Machine Reading
Zhang, Z., Ouyang, S., Zhao, H., Utiyama, M., & Sumita, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.299 · Cited by 4
Survey of Machine Reading Comprehension Models and its Evaluation Metrics
Therasa, M., & Mathivanan, G. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9754070 · Cited by 4
Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages
Gou, Q., Xia, Z., & Du, W. (2023) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2023.dialdoc-1.1 · Cited by 4
Metadata
- Authors
- Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., & Riedel, S.
- Affiliations
- University of California, Irvine (US)
- University College London (GB)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 2087-2097
- OpenAlex
- W2891304738
- Cited by
- 141
- References
- 33
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 22 · 2022: 23 · 2021: 28 · 2020: 26 · 2019: 22 · 2018: 3
- FWCI
- 8.06
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- natural language rules, rule interpretation, question answering, rule-based question answering, chatbots
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 ›
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