Search Results for author: Craig Thomson

Found 8 papers, 3 papers with code

Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text

1 code implementation INLG (ACL) 2020 Craig Thomson, Zhijie Zhao, Somayajulu Sripada

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.

Data-to-Text Generation

Shared Task on Evaluating Accuracy

no code implementations INLG (ACL) 2020 Ehud Reiter, Craig Thomson

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.

Generation Challenges: Results of the Accuracy Evaluation Shared Task

1 code implementation INLG (ACL) 2021 Craig Thomson, Ehud Reiter

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.

Shared Task on Evaluating Accuracy in Natural Language Generation

no code implementations22 Jun 2020 Ehud Reiter, Craig Thomson

We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts.

Text Generation

Comprehension Driven Document Planning in Natural Language Generation Systems

no code implementations WS 2018 Craig Thomson, Ehud Reiter, Somayajulu Sripada

This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader.

Text Generation

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