Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling

Understand

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.