Recurrent Neural Network (RNN) and one of its specific architectures, Long
Short-Term Memory (LSTM), have been widely used for sequence labeling. In this
paper, we first enhance LSTM-based sequence labeling to explicitly model label
dependencies. Then we propose another enhancement to incorporate the global
information spanning over the whole input sequence. The latter proposed method,
encoder-labeler LSTM, first encodes the whole input sequence into a fixed
length vector with the encoder LSTM, and then uses this encoded vector as the
initial state of another LSTM for sequence labeling. Combining these methods,
we can predict the label sequence with considering label dependencies and
information of whole input sequence. In the experiments of a slot filling task,
which is an essential component of natural language understanding, with using
the standard ATIS corpus, we achieved the state-of-the-art F1-score of 95.66%.

该论文提出了一种名为 Encoder-Labeler LSTM 的序列标注增强方法，通过对标签依赖性和整个输入序列信息的显式建模，较传统方法在自然语言理解中的 Slot Filling 任务中的表现有了显著的提高。