论文标题
计算效率高的命名实体识别标签者的限制解码
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
论文作者
论文摘要
指定实体识别（NER）的当前最新模型是具有条件随机场（CRF）作为最终层的神经模型。实体被表示为具有特殊结构的坦率标签，以将其解码为跨度。当前的工作避免了关于跨度编码方案如何工作和依赖于CRF学习的先验知识，哪些过渡是非法的，哪些不是促进全球连贯性的。我们发现，通过限制输出以抑制非法过渡，我们可以训练具有跨透镜损失的标记器，其速度是CRF的两倍，其F1的差异在统计学上无关紧要，从而有效地消除了对CRF的需求。我们分析了TAG共发生的动力学，以解释何时这些约束最有效，并在Pytorch和Tensorflow中提供了我们标记的开源实现。
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special structure in order to decode them into spans. Current work eschews prior knowledge of how the span encoding scheme works and relies on the CRF learning which transitions are illegal and which are not to facilitate global coherence. We find that by constraining the output to suppress illegal transitions we can train a tagger with a cross-entropy loss twice as fast as a CRF with differences in F1 that are statistically insignificant, effectively eliminating the need for a CRF. We analyze the dynamics of tag co-occurrence to explain when these constraints are most effective and provide open source implementations of our tagger in both PyTorch and TensorFlow.
