Most relevant to this work are structured prediction models capturing dependencies between decisions, either by modeling the dependencies between the hidden representations of connected decisions using RNN/LSTM Katiyar and Cardie, 2016), by explicitly modeling the structural dependencies between output predictions (Durrett and Klein, 2015;Lample et al., 2016;Andor et al., 2016), or by combining the two approaches (Socher et al., 2013;Wiseman and Rush, 2016).
in our model, illustrated in Figure 1b, the encoder is a CRF model with neural networks as its potential extractors, while the decoder is a generative model, trying to reconstruct the input.