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 thebenepar_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#
[x] Regression unit tests pass
[x] 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