aisingapore / RST-pointer

RST Pointer

You can test the model at Discourse Parsing .
If you want to find out more information, please contact us at [email protected] .

Table of Contents

• Model Details
• How to Get Started With the Model
• Training
• Model Parameters
• Other Information
• License

Model Details

Model Name: RST-Pointer

• Description: This is a pointer network-based segmenter and parser that is trained to identify the relations between different sections of a sentence according to rhetorical structure theory (RST).
• Paper: A Unified Linear-Time Framework for Sentence-Level Discourse Parsing. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, July 2019 (pp. 4190-4200).
• Author(s): Lin, X., Joty, S., Jwalapuram, P., & Bari, M. S. (2019).
• URL: https://aclanthology.org/P19-1410/

How to Get Started With the Model

Install Python package

SGnlp is an initiative by AI Singapore's NLP Hub. They aim to bridge the gap between research and industry, promote translational research, and encourage adoption of NLP techniques in the industry.

Various NLP models, other than aspect sentiment analysis are available in the python package. You can try them out at SGNLP-Demo | SGNLP-Github .

pip install sgnlp

Examples

For more full code (such as RST-Pointer), please refer to this github .
Alternatively, you can also try out the demo for Discourse-Parsing.

Example of RST-Pointer modelling on Discourse Parsing:

from sgnlp.models.rst_pointer import (
    RstPointerParserConfig,
    RstPointerParserModel,
    RstPointerSegmenterConfig,
    RstPointerSegmenterModel,
    RstPreprocessor,
    RstPostprocessor
)

# Load processors and models
preprocessor = RstPreprocessor()
postprocessor = RstPostprocessor()

segmenter_config = RstPointerSegmenterConfig.from_pretrained(
    'https://storage.googleapis.com/sgnlp-models/models/rst_pointer/segmenter/config.json')
segmenter = RstPointerSegmenterModel.from_pretrained(
    'https://storage.googleapis.com/sgnlp-models/models/rst_pointer/segmenter/pytorch_model.bin',
    config=segmenter_config)
segmenter.eval()

parser_config = RstPointerParserConfig.from_pretrained(
    'https://storage.googleapis.com/sgnlp-models/models/rst_pointer/parser/config.json')
parser = RstPointerParserModel.from_pretrained(
    'https://storage.googleapis.com/sgnlp-models/models/rst_pointer/parser/pytorch_model.bin',
    config=parser_config)
parser.eval()

sentences = [
    "Thumbs began to be troublesome about 4 months ago and I made an appointment with the best hand surgeon in the "
    "Valley to see if my working activities were the problem.",
    "Every rule has exceptions, but the tragic and too-common tableaux of hundreds or even thousands of people "
    "snake-lining up for any task with a paycheck illustrates a lack of jobs, not laziness."
]

tokenized_sentences_ids, tokenized_sentences, lengths = preprocessor(sentences)

segmenter_output = segmenter(tokenized_sentences_ids, lengths)
end_boundaries = segmenter_output.end_boundaries

parser_output = parser(tokenized_sentences_ids, end_boundaries, lengths)

trees = postprocessor(sentences=sentences, tokenized_sentences=tokenized_sentences,
                      end_boundaries=end_boundaries,
                      discourse_tree_splits=parser_output.splits)

Training

The dataset (RST Discourse Treebank) that the model is trained on is a licensed dataset.

• Training Config: Segmenter | Parser

Training Results

• Training Time (Segmenter): ~2 hours for 100 epochs on a single V100 GPU for segmenter model.
• Training Time (Parser): ~6 hours for 200 epochs on a single V100 GPU for parser model

Model Parameters

• Model Weights: Segmenter | Parser
• Model Config: Segmenter | Parser
• Model Inputs: A sentence.
• Model Outputs: Discourse parsed tree.
• Model Size: ~362MB for segmenter model, ~361MB for parser model
• Model Inference Info: Not available.
• Usage Scenarios: Construct additional features for downstream NLP tasks.

Other Information

• Original Code: link

License

• Model: Released under CC BY-NC-SA 4.0
• Code: Released under MIT License