In the present paper, two models are presented namely LSTM-CRF and
BERT-LSTM-CRF for semantic tagging of universal semantic tag dataset. The
experiments show that the first model is much easier to converge while the
second model that leverages BERT embedding, takes a long time to converge and
needs a big dataset for semtagging to be effective.

本文提出了两种模型，分别是 LSTM-CRF 和 BERT-LSTM-CRF，用于语义标记通用语义标记数据集。实验结果表明，第一个模型更易于收敛，而利用 BERT 嵌入的第二个模型需要长时间才能收敛，并需要大型数据集才能有效地进行语义标记。

基于 LSTM-CRF 的语义标注

Semantic Tagging with LSTM-CRF

In this paper, we propose second-order graph-based neural dependency parsing
using message passing and end-to-end neural networks. We empirically show that
our approaches match the accuracy of very recent state-of-the-art second-order
graph-based neural dependency parsers and have significantly faster speed in
both training and testing. We also empirically show the advantage of
second-order parsing over first-order parsing and observe that the usefulness
of the head-selection structured constraint vanishes when using BERT embedding.

本文提出了一种使用信息传递和端到端神经网络进行二阶图形神经依赖解析的方法，说明了我们的方法与最近最先进的二阶图形神经依赖解析器的准确度相匹配，并且在训练和测试中速度显著更快。同时，实证表明，相较于一阶解析，二阶解析的优势与使用 BERT 嵌入时的头部选择结构约束的有用性相似。