论文标题
自回归结构化预测的不确定性估计
Uncertainty Estimation in Autoregressive Structured Prediction
论文作者
论文摘要
不确定性估计对于确保AI系统的安全性和鲁棒性很重要。尽管该地区的大多数研究都集中在未结构化的预测任务上，但有限的工作研究了结构化预测的一般不确定性估计方法。因此，这项工作旨在调查单个统一且可解释的基于概率集成框架内自回归结构化预测任务的不确定性估计。我们考虑：在令牌级别和完整序列级别上序列数据的不确定性估计；各种不确定性措施的解释和应用；并讨论与获得相关的理论和实践挑战。这项工作还为令牌级别和序列级别的错误检测提供了基准，以及序列级别的序列级别的输入检测检测，在WMT'14英语 - 范围内和WMT'17英语 - 德语翻译和LibrisPeech语音识别数据集上。
Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for autoregressive structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT'14 English-French and WMT'17 English-German translation and LibriSpeech speech recognition datasets.
