A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing
By Hao Zhou, Chuan Cheng, Shujian Huang, Xinyu Dai, Jiajun Chen. 2017
9 citations recorded by the source corpus.
We propose a neural probabilistic structured-prediction method for transition-based natural language processing, which integrates beam search and contrastive learning. The method uses a global optimization model, which can leverage arbitrary features over non-local context. Beam search is used for efficient heuristic decoding, and contrastive learning is performed for adjusting the model according to search errors. When evaluated on both chunking and dependency parsing tasks, the proposed method achieves significant accuracy improvements over the locally normalized greedy baseline on the two tasks, respectively.
Subfields: Artificial Intelligence
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
- Devamanyu Hazarika
- Soujanya Poria
- Erik Cambria
- Tom Young
- Navonil Majumder
- Louis-philippe Morency
- Amir Zadeh
- Rada Mihalcea
Citations in the corpus
- Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “9 (UAS/LAS) [136] Beam contrastive model 93.”
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “[132] integrated beam search and contrastive learning for better optimization.”