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Evaluating factual accuracy in complex data-to-text
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OA
AI
DOI：10.1016/j.csl.2023.101482.png)
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摘要
En 中文
It is essential that data-to-text Natural Language Generation (NLG) systems produce texts which are factually accurate. We examine accuracy issues in the task of generating summaries of basketball games, including what accuracy means in this context, how accuracy errors can be detected by human annotators, as well as the types of accuracy mistakes made by both neural NLG systems and human authors. We also look at the effectiveness of automatic metrics in measuring factual accuracy.
Keyword:
Natural Language Generation
Complex data -to -text
Evaluation
Annotation
Factual accuracy
Neural data -to -text
AI总结
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期刊
C
IF:
3.4
论文数:
1.5K
被引数:
2.6K
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引用论文
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