我们需要谈论标准分割
1City University of New York.
概括
语音和语言技术中的系统排名通常不可靠. 使用随机生成的数据分割对于对部分语音标记器性能进行强有力的评估和确保可重现的结果至关重要.
科学领域:
- 语音和语言技术 语言技术
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
背景情况:
- 标准实践包括在持有测试集上评估系统.
- 在系统比较中,统计学意义和排名稳定性往往被忽视.
- 最先进的说法经常基于特定的,非随机的数据分割.
研究的目的:
- 调查语音和语言技术系统排名的可靠性.
- 评估不同数据分割策略对评估结果的影响.
- 为可重复的系统比较提出最佳实践建议.
主要方法:
- 在九个部分语音标记器上进行复制和复制实验.
- 评估系统使用广泛使用的"标准分割"和随机生成的分割.
- 在不同的实验设置中比较系统性能排名.
主要成果:
- 当使用随机生成的分区时,系统排名无法可靠地复制.
- 排名的稳定性因数据分割方法而异.
- 一些基于标准分割的最先进的索赔在随机分割下是可疑的.
结论:
- 使用标准的,非随机分割可能会导致不可靠的系统比较.
- 推随机生成的分区,以便对语音和语言技术系统进行更强大,更可重复的评估.
- 未来的研究应该优先考虑确保系统排名稳定性和可重复性的方法.
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