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 currently available datasets for training question generation systems are crowd-sourced, which largely affects the quality of generated questions.
“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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Learning to Identify Follow-Up Questions in Conversational Question Answering
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Open-Retrieval Conversational Machine ReadingPreprint
Gao, Y., Li, J., Wu, C.-S., Lyu, M. R.-T., & King, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08633 · Cited by 14 · see citing works
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
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Varifocal Question Generation for Fact-checking
Ousidhoum, N. D., Yuan, Z., & Vlachos, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.163 · Cited by 12 · see citing works
mt RAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
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Hong, T., Cho, J., Yu, H., Ko, Y., & Seo, J. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101460 · Cited by 11 · see citing works
DaNetQA: A Yes/No Question Answering Dataset for the Russian Language
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ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
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Factoring Statutory Reasoning as Language Understanding Challenges
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Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
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Development of system for generating questions, answers, distractors using transformers
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A Survey of Document Grounded Dialogue Systems (DGDS)Preprint
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More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question AnsweringPreprint
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Knowledge Distillation Across Vision and Language
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Directional attention weaving for text-grounded conversational question answering
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Gamification Platform for Collecting Task-oriented Dialogue Data
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DG2: Data Augmentation Through Document Grounded Dialogue Generation
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MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation SystemsPreprint
Katsis, Y., Rosenthal, S., Fadnis, K., Gunasekara, C., Lee, Y., Popa, L., Shah, V., Zhu, H., Contractor, D., & Danilevsky, M. (2025) · Qeios · doi:10.32388/phs6yn · Cited by 9 · see citing works
Question Answering over Curated and Open Web Sources
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Generating Information-Seeking Conversations from Unlabeled Documents
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InSCIt : Information-Seeking Conversations with Mixed-Initiative Interactions
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Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents
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Does Structure Matter? Encoding Documents for Machine Reading Comprehension
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CookDial: a dataset for task-oriented dialogs grounded in procedural documents
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Evaluating Rewards for Question Generation ModelsPreprint
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Self-citationDiscern: Discourse-Aware Entailment Reasoning Network for Conversational Machine ReadingPreprint
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Natural Language QA Approaches using Reasoning with External KnowledgePreprint
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Iterative Hierarchical Attention for Answering Complex Questions over Long DocumentsPreprint
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DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling
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Smoothing Dialogue States for Open Conversational Machine Reading
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Survey of Machine Reading Comprehension Models and its Evaluation Metrics
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Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages
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- 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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