Name
Title | ||
|---|---|---|
An Attention-Aware Bidirectional Multi-Residual Recurrent Neural Network (Abmrnn): A Study About Better Short-Term Text Classification |
Abstract | ||
|---|---|---|
Long Short-Term Memory (LSTM) has been proven an efficient way to model sequential data, because of its ability to overcome the gradient diminishing problem during training. However, due to the limited memory capacity in LSTM cells, LSTM is weak in capturing long-time dependency in sequential data. To address this challenge, we propose an Attention-aware Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. Our model considers both past and future information at every time step with omniscient attention based on LSTM. In addition to that, the multi-residual mechanism has been leveraged in our model which aims to model the relationship between current time step with further distant time steps instead of a just previous time step. The results of experiments show that our model achieves state-of-the-art performance in classification tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682565 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Long Short-Term Memory, recurrent neural network, attention model, natural language processing, residual network | Sequential data,Residual,Pattern recognition,Task analysis,Computer science,Recurrent neural network,Feature extraction,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Wang | 1 | 3 | 1.44 |
| Han Wang | 2 | 66 | 32.33 |
| Xinxiang Zhang | 3 | 5 | 2.15 |
| Theodora Chaspari | 4 | 38 | 19.43 |
| Yoonsuck Choe | 5 | 234 | 42.28 |
| Mi Lu | 6 | 37 | 6.59 |