[ACL 20] Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction

Overview

REval

Table of Contents

🎓   Introduction

REval is a simple framework for probing sentence-level representations of Relation Extraction models.

  Requirements

REval is tested with:

  • Python 3.7

🚀   Installation

With pip

<TBD>

From source

git clone https://github.com/DFKI-NLP/REval
cd REval
pip install -r requirements.txt

🔬   Probing

Supported Datasets

  • SemEval 2010 Task 8 (CoreNLP annotated version) [LINK]
  • TACRED (obtained via LDC) [LINK]

Probing Tasks

Task SemEval 2010 TACRED
ArgTypeHead ✔️ ✔️
ArgTypeTail ✔️ ✔️
Length ✔️ ✔️
EntityDistance ✔️ ✔️
ArgumentOrder ✔️
EntityExistsBetweenHeadTail ✔️ ✔️
PosTagHeadLeft ✔️ ✔️
PosTagHeadRight ✔️ ✔️
PosTagTailLeft ✔️ ✔️
PosTagTailRight ✔️ ✔️
TreeDepth ✔️ ✔️
SDPTreeDepth ✔️ ✔️
ArgumentHeadGrammaticalRole ✔️ ✔️
ArgumentTailGrammaticalRole ✔️ ✔️

🔧   Usage

Step 1: create the probing task datasets from the original datasets.

SemEval 2010 Task 8

python reval.py generate-all-from-semeval \
    --train-file <SEMEVAL DIR>/train.json \
    --validation-file <SEMEVAL DIR>/dev.json \
    --test-file <SEMEVAL DIR>/test.json \
    --output-dir ./data/semeval/

TACRED

python reval.py generate-all-from-tacred \
    --train-file <TACRED DIR>/train.json \
    --validation-file <TACRED DIR>/dev.json \
    --test-file <TACRED DIR>/test.json \
    --output-dir ./data/tacred/

Step 2: Run the probing tasks on a model.

For example, download a Relation Extraction model trained with RelEx, e.g., the CNN trained on SemEval.

mkdir -p models/cnn_semeval
wget --content-disposition https://cloud.dfki.de/owncloud/index.php/s/F3gf9xkeb2foTFe/download -P models/cnn_semeval
python probing_task_evaluation.py \
    --model-dir ./models/cnn_semeval/ \
    --data-dir ./data/semeval/ \
    --dataset semeval2010 \
    --cuda-device 0 \
    --batch-size 64 \
    --cache-representations

After the run is completed, the results are stored to probing_task_results.json in the model-dir.

{
    "ArgTypeHead": {
        "acc": 75.82,
        "devacc": 78.96,
        "ndev": 670,
        "ntest": 2283
    },
    "ArgTypeTail": {
        "acc": 75.4,
        "devacc": 78.79,
        "ndev": 627,
        "ntest": 2130
    },
    [...]
}

📚   Citation

If you use REval, please consider citing the following paper:

@inproceedings{alt-etal-2020-probing,
    title={Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction},
    author={Christoph Alt and Aleksandra Gabryszak and Leonhard Hennig},
    year={2020},
    booktitle={Proceedings of ACL},
    url={https://arxiv.org/abs/2004.08134}
}

📘   License

REval is released under the terms of the MIT License.

Owner
Speech and Language Technology (SLT) Group of the Berlin lab of the German Research Center for Artificial Intelligence (DFKI)
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create

Vector AI 267 Dec 23, 2022
[NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature"

IP-IRM [NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature". Codes will be relea

Wang Tan 67 Dec 24, 2022
[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

SSUL - Official Pytorch Implementation (NeurIPS 2021) SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sun

Clova AI Research 44 Dec 27, 2022
Nodule Generation Algorithm Baseline and template code for node21 generation track

Nodule Generation Algorithm This codebase implements a simple baseline model, by following the main steps in the paper published by Litjens et al. for

node21challenge 10 Apr 21, 2022
An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics.

Sketch Simulator An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics. See

12 Dec 18, 2022
This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.

GANs for Fun Created because I can! GOAL The goal of this repo is to be freely used by ML devs to check the GAN performances without coding from scrat

Sagnik Roy 13 Jan 26, 2022
ByteTrack(Multi-Object Tracking by Associating Every Detection Box)のPythonでのONNX推論サンプル

ByteTrack-ONNX-Sample ByteTrack(Multi-Object Tracking by Associating Every Detection Box)のPythonでのONNX推論サンプルです。 ONNXに変換したモデルも同梱しています。 変換自体を試したい方はByteT

KazuhitoTakahashi 16 Oct 26, 2022
Projecting interval uncertainty through the discrete Fourier transform

Projecting interval uncertainty through the discrete Fourier transform This repo

1 Mar 02, 2022
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Jack Turner 12 Nov 05, 2022
Official PyTorch implementation of "RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on" (IJCAI-ECAI 2022)

RMGN-VITON RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on In IJCAI-ECAI 2022(short oral). [Paper] [Supplementary Material] Abstra

27 Dec 01, 2022
object recognition with machine learning on Respberry pi

Respberrypi_object-recognition object recognition with machine learning on Respberry pi line.py 建立一支與樹梅派連線的 linebot 使用此 linebot 遠端控制樹梅派拍照 config.ini l

1 Dec 11, 2021
ProMP: Proximal Meta-Policy Search

ProMP: Proximal Meta-Policy Search Implementations corresponding to ProMP (Rothfuss et al., 2018). Overall this repository consists of two branches: m

Jonas Rothfuss 212 Dec 20, 2022
Pure python PEMDAS expression solver without using built-in eval function

pypemdas Pure python PEMDAS expression solver without using built-in eval function. Supports nested parenthesis. Supported operators: + - * / ^ Exampl

1 Dec 22, 2021
A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling"

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

Zekarias Tilahun 24 Jun 21, 2022
Earth Vision Foundation

EVer - A Library for Earth Vision Researcher EVer is a Pytorch-based Python library to simplify the training and inference of the deep learning model.

Zhuo Zheng 34 Nov 26, 2022
Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders"

AAVAE Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders" Abstract Recent methods for self-supervised learnin

Grid AI Labs 48 Dec 12, 2022
An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

DeepNER An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models. This repository contains complex Deep

Derrick 9 May 30, 2022
Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints". Edit 2021/

10 Dec 20, 2022
Cowsay - A rewrite of cowsay in python

Python Cowsay A rewrite of cowsay in python. Allows for parsing of existing .cow

James Ansley 3 Jun 27, 2022