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Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e.
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
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Earlier work this paper cites.
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F. Mairesse, M. Gašić, F. Jurčíček, S. Keizer, B. Thomson, K. Yu, and S. Young. 2010 · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
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Challenges in Data-to-Document Generation
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Findings of the E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2018 · 2018
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Pathologies of Neural Models Make Interpretations Difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
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A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
Juraj Juraska, Panagiotis Karagiannis, Kevin K. Bowden, and Marilyn A. Walker. 2018 · 2018
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Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?
Lena Reed, Shereen Oraby, and Marilyn Walker. 2018 · 2018
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Cited alongside, same era.
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, and Steve Young. 2016 · 2016
Cited alongside, same era.
The Extended SPaRKy Restaurant Corpus: Designing a Corpus with Variable Information Density
David M. Howcroft, Dietrich Klakow, and Vera Demberg. 2017 · 2017
Cited alongside, same era.
Six Challenges for Neural Machine Translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
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Object hallucination in image captioning
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Hallucinations in neural machine translation
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A simple recipe towards reducing hallucination in neural surface realisation
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