Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Last update: Nov 08, 2022

Overview

crfsrl

Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Zhenghua Li, Guohong Fu, Min Zhang. Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments. 2021. [arxiv]

Setup

The following packages should be installed:

SuPar: == 1.1.3
PyTorch: >= 1.7
Transformers: >= 4.0

Run the following scripts to obtain the training data. Please make sure PTB and OntoNotes are available:

bash scripts/conll05.sh PTB=<path-to-ptb>             SRL=data
bash scripts/conll12.sh ONTONOTES=<path-to-ontonotes> SRL=data

Run

Try the following commands to train first-order CRF and second-order CRF2o models:

# LSTM
# CRF
python -u crf.py   train -b -c configs/conll05.crf.srl.lstm.char-lemma.ini   -d 0 -f char lemma -p exp/conll05.crf.srl.lstm.char-lemma/model
# CRF2o
python -u crf2o.py train -b -c configs/conll05.crf2o.srl.lstm.char-lemma.ini -d 0 -f char lemma -p exp/conll05.crf2o.srl.lstm.char-lemma/model
# BERT finetuning
# CRF
python -u crf.py   train -b -c configs/conll05.crf.srl.bert.ini   -d 0 -p exp/conll05.crf.srl.bert/model   --batch-size=1000 --encoder bert --bert bert-large-cased 
# CRF2o
python -u crf2o.py train -b -c configs/conll05.crf2o.srl.bert.ini -d 0 -p exp/conll05.crf2o.srl.bert/model --batch-size=1000 --encoder bert --bert bert-large-cased

To do evaluation:

# end-to-end
python -u crf.py   evaluate -c configs/conll05.crf.srl.bert.ini  -d 0 -p exp/conll05.crf.srl.bert/model    --data data/conll05/test.conllu
# w/ gold predicates
python -u crf.py   evaluate -c configs/conll05.crf.srl.bert.ini  -d 0 -p exp/conll05.crf.srl.bert/model    --data data/conll05/test.conllu --prd

To make predictions:

python -u crf.py   predict  -c configs/conll05.crf.srl.bert.ini   -d 0 -p exp/conll05.crf.srl.bert/model   --data data/conll05/test.conllu --pred pred.conllu
bash scripts/eval.sh pred=pred.conllu gold=data/conll05/test.conllu

Contact

If you have any questions, feel free to contact me via emails.

Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Related tags

Overview

crfsrl

Setup

Run

Contact

Owner

Yu Zhang

Transformers-regression - Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates

Topic Modelling for Humans

MRC approach for Aspect-based Sentiment Analysis (ABSA)

Multiple implementations for abstractive text summurization , using google colab

Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

Codename generator using WordNet parts of speech database

DeepAmandine is an artificial intelligence that allows you to talk to it for hours, you won't know the difference.

Simple Annotated implementation of GPT-NeoX in PyTorch

👑 spaCy building blocks and visualizers for Streamlit apps

Officile code repository for "A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning"

Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation

topic modeling on unstructured data in Space news articles retrieved from the Guardian (UK) newspaper using API

Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

EMNLP 2021 paper "Pre-train or Annotate? Domain Adaptation with a Constrained Budget".

Treemap visualisation of Maya scene files

Python library for processing Chinese text

Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

Dust model dichotomous performance analysis

Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

A sentence aligner for comparable corpora