Kitaev et al. (2019)

Publication

Constituency Parsing with a Self-Attentive Encoder

Multilingual Constituency Parsing with Self-Attention and Pre-Training

Repositories

https://github.com/nikitakit/self-attentive-parser

Available Models

Benepar
- Description: A wrapper around the Benepar parser
- Name: kitaev2019-benepar
- Usage:
```
from repro.models.kitaev2019 import Benepar
model = Benepar()
trees = model.predict("The time for action is now.")
```
  trees is a list of strings which contains the serialized parse trees for the input text.

Implementation Notes

The input text does not have to be a single sentence. A parse tree will be returned for each one based on the library's sentence splitting logic.

Docker Information

Image name: danieldeutsch/kitaev2019
Build command:
```
repro setup kitaev2019 [--models <model-name>+] [--silent]
```
The --models argument specifies which pretrained parsing models should be included in the Docker image. By default, only the benepar_en3 model is included. The list of available models can be found here.
Requires network: No

Testing

repro setup kitaev2019
pytest models/kitaev2019/tests

Status

Regression unit tests pass
Correctness unit tests pass
We verify the parser returns the example from the Github repo. See here.
Model runs on full test dataset
Not tested
Predictions approximately replicate results reported in the paper
Not tested
Predictions exactly replicate results reported in the paper
Not tested

Changelog