Length Adaptive Recurrent Model for Text Classification

By Zhengjie Huang, Zi Ye, Shuangyin Li, Rong Pan. 2017

24 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shiji Song
2. Yulin Wang
3. Gao Huang
4. Le Yang
5. Yizeng Han
6. Honghui Wang
7. Quoc V. Le
8. Alan L. Yuille

In Natural Language Processing

1. Gao Huang
2. Shiji Song
3. Yulin Wang

In Model Architectures

1. Shiji Song
2. Yulin Wang
3. Gao Huang
4. Honghui Wang
5. Le Yang
6. Yizeng Han
7. Quoc V. Le
8. Alan L. Yuille

Citations in the corpus

• Dynamic Neural Networks: A Survey (2021, background): “Similarly, early stopping is implemented for sentence-level [174] and paragraph-level [65] text classification, respectively.”
• Neural Speed Reading with Structural-Jump-LSTM (2019, unknown): “…word: i) One or more words can be jumped over, e.g. as done in our model, LSTM-Jump (Yu et al., 2017), Yu-LSTM (Yu et al., 2018) and Adaptive-LSTM (Huang et al., 2017), where the latter implements a jump as early stopping. ii) A word can be skipped or skimmed, where the model is aware of the word…”