SOTA
Performance: Highest F1 Score/ UAS(LAS)
- Constituency Parsing with a Self-Attentive Encoder
- Deep Biaffine Attention for Neural Dependency Parsing
Speed: Fastest
This repo's target
Faster and Accurate Syntactic Parsing both on Constituency and Dependency.
Naive Motivation
Conversion: Dependency Tree could be converted from constituency by utilizing head rule.
Span: where the head rule is used.
Implementation
Self-Attentive Con Parser, start from scratch with:
- Phrase Structure Tree Load/Conversion/Expr, Oracle Design
- Pretrain Model Loaded: ELMO
- Multi-Head Self-Attentive Encoder
- Max-Span Tree Inference
- Training
Biaffine Dep Parser, start from scratch with:
- Dependency Relation Tree Load/Conversion/Expr, Oracle Design
- Pretrain Word Embedding Loaded
- Bi-LSTM Encoder
- MST Inference with Attention Module
- Training
FAParser's new features:
- Enhanced Pretrain Module of Word Representation
- Enhanced MST Inference with Multi-Head Attention Module
- Conditional modeling on interaction between information flows of phrase structure and dependency relation
Similar to the design of fairseq, we organize our FAParser as:
FAParser
│ README.md
│ train.py
│ inference.py
│ preprocess.py
│
└───evaluation: for validation or testing
│ │ F1
│ │ Accuracy
│ │ │ UAS
│ │ └ LAS
│ └ ...
│
└───data:
│ │ tree loaded or structure utils
│ │
│ └ ...
│
└───criterion:
│ │ cross entropy. etc
│ │
│ └ ...
│
└───models:
│ │ three parser
│ │
│ └ ...
│
└───modules:
│ │ series of module used in models
│ │
│ └ ...
│
└───optim: for optimizer
│ │ lr_shedule
│ │ adam...
│ └ ...
│
└───tasks: for loss computing
│ │ Constituency Parser
│ │ Dependenecy Parser
│ │ FAParser
│ └ ...
│
└───utlis:
│ │ command/preprocess/meters...
│ │
│ └ ...
criterion,modules,optim and data could be initialized with the fairseq's sub-modules. we just need build some task-specific sub-packages.