Craig Thomson


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.

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.