amrlib 0.1.0

A python library that makes AMR parsing, generation and visualization simple.

amrlib

A python library that makes AMR parsing, generation and visualization simple.

About

amrlib is a python module designed to make processing for Abstract Meaning Representation (AMR) simple by providing the following functions

• Sentence to Graph (StoG) parsing to create AMR graphs from English sentences.
• Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences.
• A QT based GUI to facilitate conversion of sentences to graphs and back to sentences
• Methods to plot AMR graphs in both the GUI and as library functions
• Training and test code for both the StoG and GtoS models.
• A SpaCy extension that allows direct conversion of SpaCy Docs and Spans to AMR graphs.

AMR Models

The system uses two different Neural Network models for parsing and generation.

The parsing (StoG) model comes from AMR-gs, the details of which can be found in this paper. The version of the model used here eliminates much of the data abstraction (aka anonymization) used in the original code. During testing, this model achieves a 77 SMATCH score with LDC2020T02.

The generation (GtoS) model takes advantage of the pretrained HuggingFace T5 transformer. The model is fine-tuned to translate AMR graphs to English sentences. The retrained model achieves a BLEU score of 43 with LDC2020T02.

Documentation

For the latest documentation, see ReadTheDocs.

AMR View

The GUI allows for simple viewing, conversion and plotting of AMR Graphs.

Requirements and Installation

The project was built and tested under Python 3 and Ubuntu but should run on any Linux, Windows, Mac, etc.. system.

To install do..

pip3 install -r requirements.txt

pip3 install amrlib

Note that installing amrlib will automatically install a minimal set of requirements but for the QT based amr_view or to test/train a model, you'll need to also install from the requirements.txt file.

To install the pretrained models do..

import amrlib
amrlib.download('model_stog', stog_url)    <-- stog_url below
amrlib.download('model_gtos', gtos_url)    <-- gtos_url below

stog_url =
'https://p-def8.pcloud.com/cBZLUUPPBZBfnwosZZZT4y137Z2ZZe3VZkZYRHagZC7ZVpZBHZyFZ9pZI0ZhXZU7ZYZdkZh7ZrFZCFZIpZD2z0XZTS00VM2QHM4XvD8cvftRmB8ghiTk/model_parse_gsii-v0_1_0.tar.gz'

gtos_url =
'https://p-def5.pcloud.com/cBZ9VvYPBZ56xFosZZZCLy137Z2ZZe3VZkZ2LOTcZzVZ40ZlkZVHZBFZu0ZaJZnJZEpZP5Z4pZokZcJZuJZF2z0XZtjveznPmwmm9KNc7cg0rRurX0Lnk/model_generate_t5-v0_1_0.tar.gz'

!! If you're get errors trying to download the models, email me at [e-mail] and I'll email back a link from pcloud that you can use to download them manually.

The code base also includes library functions and scripts to train and test the parsing and generation nets. The scripts to do this are included in the scripts directory which is not part of the pip installation. If you want to train the networks, it is recommended that you download or clone the source code and use it in-place.

Library Usage

To convert sentences to grahs

import amrlib
stog = amrlib.load_stog_model()
graphs = stog.parse_sents(['This is a test of the system.', 'This is a second sentence.'])
for graph in graphs:
    print(graph)

To convert graphs to sentences

import amrlib
gtos = amrlib.load_gtos_model()
sents, _ = gtos.generate(graphs, disable_progress=True)
for sent in sents:
    print(sent)

For a detailed description see the User API.

Usage as a Spacy Extension

To use as an extension, you need spaCy version 2.0 or later. To setup the extension and use it do the following

import amrlib
import spacy
amrlib.setup_spacy_extension()
nlp = spacy.load('en')
doc = nlp('This is a test of the SpaCy extension. The test has multiple sentences.')
graphs = doc._.to_amr()
for graph in graphs:
    print(graph)

Issues

If you find a bug, please report it on the GitHub issues list. Additionally, if you have feature requests or questions, feel free to post there as well. I'm happy to consider suggestions and Pull Requests to enhance the functionality and usability of the module.