Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing
This work addresses a specific issue in natural language processing for Chinese dependency parsing, representing an incremental improvement over traditional methods.
The authors tackled the problem of training transition-based dependency parsers by proposing a Hybrid Oracle that leverages ambiguity in correct transition sequences, resulting in improved performance for Chinese dependency parsing.
In the training of transition-based dependency parsers, an oracle is used to predict a transition sequence for a sentence and its gold tree. However, the transition system may exhibit ambiguity, that is, there can be multiple correct transition sequences that form the gold tree. We propose to make use of the property in the training of neural dependency parsers, and present the Hybrid Oracle. The new oracle gives all the correct transitions for a parsing state, which are used in the cross entropy loss function to provide better supervisory signal. It is also used to generate different transition sequences for a sentence to better explore the training data and improve the generalization ability of the parser. Evaluations show that the parsers trained using the hybrid oracle outperform the parsers using the traditional oracle in Chinese dependency parsing. We provide analysis from a linguistic view. The code is available at https://github.com/lancopku/nndep .