We explore the possibility of improving probabilistic models in structured
prediction. Specifically, we combine the models with constrained decoding
approaches in the context of token classification for information extraction.
The decoding methods search for constraint-satisfying label-assignments while
maximizing the total probability. To do this, we evaluate several existing
approaches, as well as propose a novel decoding method called Lazy-$k$. Our
findings demonstrate that constrained decoding approaches can significantly
improve the models' performances, especially when using smaller models. The
Lazy-$k$ approach allows for more flexibility between decoding time and
accuracy. The code for using Lazy-$k$ decoding can be found here:
this https URL

我们研究了在结构预测中改善概率模型的可能性。具体而言，我们将这些模型与约束解码方法相结合，用于信息提取中的标记分类。我们评估了几种现有的方法，并提出了一种名为 Lazy-$k$ 的新的解码方法。我们的研究结果表明，约束解码方法可以显著提高模型的性能，尤其是在使用较小模型时。Lazy-$k$ 方法在解码时间和准确性之间提供了更大的灵活性。可以在此链接找到使用 Lazy-$k$ 解码的代码：this https URL