San Jose State University

In transition-Based Dependency Parsing, the oracle can tell which transition is appropriate to make. It can be trained by supervised machine learning.
The oracle takes the configuration of the stack and the word list of one sentence as its input and predicts appropriate transition. 


Oracle


An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

To generate training data for Oracle, some reference parses and training oracle are needed. Training oracle, which is the model to generate training data for oracle, will 
$$\left ( 1 \right )$$ Choose LEFTARC if it produces a correct head-dependent relation given the reference parse and the current configuration.
$$\left ( 2 \right )$$ Otherwise, choose RIGHTARC if (1) it produces a correct head-dependent relation given the reference parse and (2) all of the dependents of the word at the top of the stack has already been assigned.
$$\left ( 3 \right )$$Otherwise, choose SHIFT.


Generating Training Data for Oracle


The real oracle can be a feature-based classifier using logistic regression. Features for this kind of oracle include word forms, parts of speech, case markings.


Feature-based classifier


Parser takes top two words from the stack and one word from the buffer. Encoding of these words are found out with the help of an encoder. The words and its encodings are concatenated and sent to a feed forward network to generate the prediction for transition.


Oracle Implemented by a Neural Classifier for Transition-based parser

Head words are attached to its corresponding dependent words as early as possible. The transition operations on the words are LEFTARC ,RIGHTARC, SHIFT and REDUCE.


LEFTARC: Assert a head-dependent relation between the word at the front of
the input buffer and the word at the top of the stack; pop the stack.

RIGHTARC: Assert a head-dependent relation between the word on the top of
the stack and the word at front of the input buffer; shift the word at the front
of the input buffer to the stack.

SHIFT: Remove the word from the front of the input buffer and push it onto
the stack.

REDUCE: Pop the stack.

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