Natural Language Generation

Learning to generate: Bayesian nonparametric approaches to inducing rules for natural language generation

In order for computers to produce natural language texts from non-linguistic information, we need a system for mapping between the two, …
David M. Howcroft
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Most NLG is Low-Resource: here's what we can do about it
Many domains and tasks in natural language generation (NLG) are inherently ‘low-resource’, where training data, tools and linguistic …
David M. Howcroft, Dimitra Gkatzia
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Low-resource NLG - developing tools and building a Scottish Gaelic dataset
Most NLG is low-resource, even in high-resource languages (Howcroft & Gkatzia 2022). In this talk I will highlight our efforts to develop a data collection paradigm which focuses on question answering conversations and (dialogue) summarisation for low-resource languages.
21 Nov 2022
Disentangling 20 years of confusion: the need for standards in human evaluation
Human assessment remains the most trusted form of evaluation in natural language generation, among other areas of NLP, but there is huge variation in terms of both what is assessed and how it is assessed.
9 Jul 2021
Natural Language Generation and Human Language Production: a history and an opportunity
21 May 2021
Slides
Disentangling 20 years of confusion: quo vadis, human evaluation?
29 Mar 2021
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions
Human assessment remains the most trusted forrm of evaluation in NLG, but highly diverse approaches and a proliferation of different …
David M. Howcroft, Anya Belz, Miruna Clinciu, Dimitra Gkatzia, Sadid A. Hasan, Saad Mahamood, Simon Mille, Emiel van Miltenburg, Sashank Santhanam, Verena Rieser
PDF Dataset Slides ACL Anthology
Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing
Current standards for designing and reporting human evaluations in NLP mean it is generally unclear which evaluations are comparable …
Anya Belz, Simon Mille, David M. Howcroft
PDF ACL Anthology
Semantic Noise Matters for Neural Natural Language Generation
Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to …
Ondřej Dušek, David M. Howcroft, Verena Rieser
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Noise and Neural Natural Language GenerationRubbish in, Rubbish out?
At this workshop we highlighted several sources of noise for neural NLG (semantic, typographic, and grammatical) before presenting the impact of semantic noise on the quality of NNLG (in a preview of our INLG paper) and how these different kinds of errors impact human evaluations of perceived text quality.
Ondřej Dušek, David M. Howcroft, Karin Sevegnani, Verena Rieser
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