Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is able to dynamically decide how much information to use from a
word- or character-level component. We evaluated different architectures on a
range of sequence labeling datasets, and character-level extensions were found
to improve performance on every benchmark. In addition, the proposed
attention-based architecture delivered the best results even with a smaller
number of trainable parameters.

该论文研究了序列标注结构中使用单词嵌入进行相似性捕获时处理以前未见过或罕见单词时的问题，并提出了一种新颖的架构来组合替代单词表示。通过使用注意力机制，模型能够动态决定从单词或字符级组件中使用多少信息。我们在一系列序列标注数据集上评估了不同的架构，并发现字符级扩展在每个基准测试中都提高了性能。此外，即使使用较少的可训练参数，提出的基于注意力的架构也能够提供最佳结果。