Cited by 141Topic ModelingNatural Language Processing TechniquesSpeech and dialogue systems

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Interpretation of Natural Language Rules in Conversational Machine Reading

Open access
Saeidi, Marzieh, Bartolo, Max, Lewis, Patrick, Singh, Sameer, Rocktäschel, Tim, Sheldon, Mike, Bouchard, Guillaume, Riedel, Sebastian·2018·Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Abstract

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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Crossref · 10.18653/v1/d18-1233

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Figures

Figure 1 An example of two utterances for rule interpretation. In the first utterance, a follow-up question is generated. In the second, the scenario, history and background knowledge (Canada is not in the EEA) is used to arrive at the answer 'Yes'.
Figure 1 An example of two utterances for rule interpretation. In the first utterance, a follow-up question is generated. In the second, the scenario, history and background knowledge (Canada is not in the EEA) is used to arrive at the answer 'Yes'.CC-BY · From the PDF
Figure 2 The different stages of the annotation process (excluding the rule text extraction stage). First a human annotator generates an underspecified input question (question generation). Then, a virtual user and a human annotator collaborate to produce a dialog of follow-up questions and answers (dialog generation). Finally, a scenario is generated from parts of the dialog, and these parts are omitted in the final result.
Figure 2 The different stages of the annotation process (excluding the rule text extraction stage). First a human annotator generates an underspecified input question (question generation). Then, a virtual user and a human annotator collaborate to produce a dialog of follow-up questions and answers (dialog generation). Finally, a scenario is generated from parts of the dialog, and these parts are omitted in the final result.CC-BY · From the PDF
Figure 3 We use different annotators (indicated by different colors) to create the complete dialog tree.
Figure 3 We use different annotators (indicated by different colors) to create the complete dialog tree.CC-BY · From the PDF

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Citations

Cites

Show all 33 references

Research works printed but not linked: 4

  1. Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, Jos MF Moura, Devi Parikh, and Dhruv Batra. 2017. Visual dialog. In Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2.
  2. Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word repre- sentations. arXiv preprint arXiv:1802.05365. · arXiv
  3. Joseph Weizenbaum. 1966. ELIZAa computer pro- gram for the study of natural language communica- tion between man and machine. Communications of the ACM, 9(1):36-45.
  4. Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. Constructing datasets for multi-hop reading comprehension across documents. Transac- tions of ACL, abs/1710.06481. · arXiv

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Items 101–141 of 141

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
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

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