Qiankun Fu, Linfeng Song, Wenyu Du, Yue Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently.State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem.Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing.In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling.Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.

In linear representation-based image classification, an unlabeled sample is represented by the entire training set. To obtain a stable and discriminative solution, regularization on the vector of representation coefficients is necessary. For example, the representation in sparse representation-based classification (SRC) uses L1 norm penalty as regularization, which is equal to lasso. However, lasso overemphasizes the role of sparseness while ignoring the inherent structure among samples belonging to a same class. Many recent developed representation classifications have adopted lasso-type regressions to improve the performance. In this paper, we propose the adaptive class preserving representation for classification (ACPRC). Our method is related to group lasso based classification but different in two key points: When training samples in a class are uncorrelated, ACPRC turns into SRC, when samples in a class are highly correlated, it obtains similar result as group lasso. The superiority of ACPRC over other state-of-the-art regularization techniques including lasso, group lasso, sparse group lasso, etc. are evaluated by extensive experiments.