Can I read Syntax Aware LSTM Model for Chinese Semantic Role Labeling on EtoBox?

Syntax Aware LSTM Model for Chinese Semantic Role Labeling by Qian, Feng; Sha, Lei; Chang, Baobao; Liu, Lu-chen; Zhang, Ming is a scholarly article available to read on EtoBox.

What is Syntax Aware LSTM Model for Chinese Semantic Role Labeling about?

As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way. In this paper, we propose Syntax Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies according to dependency parsing information in order to model parsing information directly in an architecture engineering way instead of feature engineering way. We experimentally demonstrate that SA-LSTM gains more improvement from the model architecture. Furthermore, SA-LSTM outperforms the state-of-the-art on CPB 1.0 significantly according to Student t-test ($p<0.05$).

Author

Qian, Feng; Sha, Lei; Chang, Baobao; Liu, Lu-chen; Zhang, Ming

Published

2017

Language

EN