/direct_parsing_to_sent_graph

Explore training on other data

Primary LanguagePython

Direct parsing to sentiment graphs

David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid and Erik Velldal

University of Oslo, Language Technology Group
University of the Basque Country UPV/EHU, HiTZ Center – Ixa
National Library of Sweden, KBLab


Paper
Pretrained models
Interactive demo on Google Colab

Overall architecture


This repository provides the official PyTorch implementation of our paper "Direct parsing to sentiment graphs" together with pretrained base models for all six datasets (TODO): Darmstadt, MPQA, Multibooked_ca, Multibooked_eu and NoReC.


How to run

🐾   Training

To train PERIN on NoReC, run the following script. Other configurations are located in the perin/config folder.

cd perin
sbatch run.sh config/seq_norec.yaml

🐾   Inference

You can run the inference on the validation and test datasets by running:

python3 inference.py --checkpoint "path_to_pretrained_model.h5" --data_directory ${data_dir}

Citation

TBA