Underreporting of errors in NLG output, and what to do about it
Underreporting of errors in NLG output, and what to do about it
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DOI：
10.18653/v1/2021.inlg-1.14
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Emiel van Miltenburg;Miruna Clinciu;Ondrej Dusek;Dimitra Gkatzia;Stephanie Inglis;Leo Leppanen;Saad Mahamood;Emma Manning;S. Schoch;Craig Thomson;Luou Wen
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文献类型：
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作者：
Emiel van Miltenburg;Miruna Clinciu;Ondrej Dusek;Dimitra Gkatzia;Stephanie Inglis;Leo Leppanen;Saad Mahamood;Emma Manning;S. Schoch;Craig Thomson;Luou Wen
我们观察到自然语言产生的不同类型的错误，这是一个问题。关于“最先进”研究所暴露的特定弱点，以量化不足的错误程度错误识别，分析和报告的建议。
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.