  Implicit discourse relation recognition (IDRR) is a challenging but crucial
task in discourse analysis. Most existing methods train multiple models to
predict multi-level labels independently, while ignoring the dependence between
hierarchically structured labels. In this paper, we consider multi-level IDRR
as a conditional label sequence generation task and propose a Label
Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we
first design a label attentive encoder to learn the global representation of an
input instance and its level-specific contexts, where the label dependence is
integrated to obtain better label embeddings. Then, we employ a label sequence
decoder to output the predicted labels in a top-down manner, where the
predicted higher-level labels are directly used to guide the label prediction
at the current level. We further develop a mutual learning enhanced training
method to exploit the label dependence in a bottomup direction, which is
captured by an auxiliary decoder introduced during training. Experimental
results on the PDTB dataset show that our model achieves the state-of-the-art
performance on multi-level IDRR. We will release our code at
https://github.com/nlpersECJTU/LDSGM.
