Depth-Adaptive Graph Recurrent Network for Text Classification
Table 2: Accuracy scores (%) on the Amazon-16 datasets. † is our implementations with several recent advanced techniques (e.g., label smoothing) under the unified setting. We establish new state-of-the-art results on 12 of total 16 datasets, and outperform the existing highest average score (+0.7%).
['Data / Model', 'MS-Trans.', 'Transformer †', 'Star-Trans. †', '3L-BiLSTMs †', 'S-LSTM †', 'RCRN', 'Ours']
S4SS3SSS0Px1 Results on Amazon-16. The average score gains over 3-layer stacked Bi-LSTMs (+1.8%), and the S-LSTM (+1.3%) are also notable. Strong baselines such as Star-Transformer Guo et al. and Recurrently Controlled Recurrent Networks (RCRN) Tay et al.