Craig Thomson


2021

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Underreporting of errors in NLG output, and what to do about it
Emiel van Miltenburg | Miruna Clinciu | Ondřej Dušek | Dimitra Gkatzia | Stephanie Inglis | Leo Leppänen | Saad Mahamood | Emma Manning | Stephanie Schoch | Craig Thomson | Luou Wen
Proceedings of the 14th International Conference on Natural Language Generation

We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make. This is a problem, because mistakes are an important indicator of where systems should still be improved. If authors only report overall performance metrics, the research community is left in the dark about the specific weaknesses that are exhibited by ‘state-of-the-art’ research. Next to quantifying the extent of error under-reporting, this position paper provides recommendations for error identification, analysis and reporting.

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Generation Challenges: Results of the Accuracy Evaluation Shared Task
Craig Thomson | Ehud Reiter
Proceedings of the 14th International Conference on Natural Language Generation

The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this task, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).

2020

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Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text
Craig Thomson | Zhijie Zhao | Somayajulu Sripada
Proceedings of the 13th International Conference on Natural Language Generation

It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds that a state-of-the-art neural data-to-text system produces higher quality output, according to the information extraction (IE) based metrics, when additional input data is carefully selected from this newly available source. It remains to be shown, however, whether IE metrics used in this study correlate well with humans in judging text quality.

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A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems
Craig Thomson | Ehud Reiter
Proceedings of the 13th International Conference on Natural Language Generation

Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.

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Shared Task on Evaluating Accuracy
Ehud Reiter | Craig Thomson
Proceedings of the 13th International Conference on Natural Language Generation

We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts, specifically summaries of basketball games produced from basketball box score and other game data. We welcome submissions based on protocols for human evaluation, automatic metrics, as well as combinations of human evaluations and metrics.

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SportSett:Basketball - A robust and maintainable data-set for Natural Language Generation
Craig Thomson | Ehud Reiter | Somayajulu Sripada
Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation

2018

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Comprehension Driven Document Planning in Natural Language Generation Systems
Craig Thomson | Ehud Reiter | Somayajulu Sripada
Proceedings of the 11th International Conference on Natural Language Generation

This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader. Ways in which cognitive theory might be combined with existing NLG techniques are discussed and two simple experiments in content ordering are presented.