A Label Dependence-Aware Sequence Generation Model for Multi-Level Implicit Discourse Relation Recognition
Keywords:Speech & Natural Language Processing (SNLP)
AbstractImplicit 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 bottom-up 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 release our code at https://github.com/nlpersECJTU/LDSGM.
How to Cite
Wu, C., Cao, L., Ge, Y., Liu, Y., Zhang, M., & Su, J. (2022). A Label Dependence-Aware Sequence Generation Model for Multi-Level Implicit Discourse Relation Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11486-11494. https://doi.org/10.1609/aaai.v36i10.21401
AAAI Technical Track on Speech and Natural Language Processing