Lexical Substitution Framework

Related tags

Deep LearningLexSubGen
Overview

LexSubGen

Lexical Substitution Framework

This repository contains the code to reproduce the results from the paper:

Arefyev Nikolay, Sheludko Boris, Podolskiy Alexander, Panchenko Alexander, "Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution", Proceedings of the 28th International Conference on Computational Linguistics, 2020

Installation

Clone LexSubGen repository from github.com.

git clone https://github.com/Samsung/LexSubGen
cd LexSubGen

Setup anaconda environment

  1. Download and install conda
  2. Create new conda environment 
    conda create -n lexsubgen python=3.7.4
  3. Activate conda environment 
    conda activate lexsubgen
  4. Install requirements 
    pip install -r requirements.txt
  5. Download spacy resources and install context2vec and word_forms from github repositories 
    ./init.sh

Setup Web Application

If you do not plan to use the Web Application, skip this section and go to the next!

  1. Download and install NodeJS and npm.
  2. Run script for install dependencies and create build files.
bash web_app_setup.sh

Install lexsubgen library

python setup.py install

Results

Results of the lexical substitution task are presented in the following table. To reproduce them, follow the instructions above to install the correct dependencies.

Model SemEval COINCO
GAP [email protected] [email protected] [email protected] GAP [email protected] [email protected] [email protected]
OOC 44.65 16.82 12.83 18.36 46.3 19.58 15.03 12.99
C2V 55.82 7.79 5.92 11.03 48.32 8.01 6.63 7.54
C2V+embs 53.39 28.01 21.72 33.52 50.73 29.64 24.0 21.97
ELMo 53.66 11.58 8.55 13.88 49.47 13.58 10.86 11.35
ELMo+embs 54.16 32.0 22.2 31.82 52.22 35.96 26.62 23.8
BERT 54.42 38.39 27.73 39.57 50.5 42.56 32.64 28.73
BERT+embs 53.87 41.64 30.59 43.88 50.85 46.05 35.63 31.67
RoBERTa 56.74 32.25 24.26 36.65 50.82 35.12 27.35 25.41
RoBERTa+embs 58.74 43.19 31.19 44.61 54.6 46.54 36.17 32.1
XLNet 59.12 31.75 22.83 34.95 53.39 38.16 28.58 26.47
XLNet+embs 59.62 49.53 34.9 47.51 55.63 51.5 39.92 35.12

Results reproduction

Here we list XLNet reproduction commands that correspond to the results presented in the table above. Reproduction commands for all models you can find in scripts/lexsub-all-models.sh Besides saving to the 'run-directory' all results are saved using mlflow. To check them you can run mlflow ui in LexSubGen directory and then open the web page in a browser.

Also you can use pytest to check the reproducibility. But it may take a long time:

pytest tests/results_reproduction

  • XLNet:

XLNet Semeval07:

python lexsubgen/evaluations/lexsub.py solve --substgen-config-path configs/subst_generators/lexsub/xlnet.jsonnet --dataset-config-path configs/dataset_readers/lexsub/semeval_all.jsonnet --run-dir='debug/lexsub-all-models/semeval_all_xlnet' --force --experiment-name='lexsub-all-models' --run-name='semeval_all_xlnet'

XLNet CoInCo:

python lexsubgen/evaluations/lexsub.py solve --substgen-config-path configs/subst_generators/lexsub/xlnet.jsonnet --dataset-config-path configs/dataset_readers/lexsub/coinco.jsonnet --run-dir='debug/lexsub-all-models/coinco_xlnet' --force --experiment-name='lexsub-all-models' --run-name='coinco_xlnet'

XLNet with embeddings similarity Semeval07:

python lexsubgen/evaluations/lexsub.py solve --substgen-config-path configs/subst_generators/lexsub/xlnet_embs.jsonnet --dataset-config-path configs/dataset_readers/lexsub/semeval_all.jsonnet --run-dir='debug/lexsub-all-models/semeval_all_xlnet_embs' --force --experiment-name='lexsub-all-models' --run-name='semeval_all_xlnet_embs'

XLNet with embeddings similarity CoInCo:

python lexsubgen/evaluations/lexsub.py solve --substgen-config-path configs/subst_generators/lexsub/xlnet_embs.jsonnet --dataset-config-path configs/dataset_readers/lexsub/coinco.jsonnet --run-dir='debug/lexsub-all-models/coinco_xlnet_embs' --force --experiment-name='lexsub-all-models' --run-name='coinco_xlnet_embs'

Word Sense Induction Results

Model SemEval 2013 SemEval 2010
AVG AVG
XLNet 33.4 52.1
XLNet+embs 37.3 54.1

To reproduce these results use 2.3.0 version of transformers and the following command:

bash scripts/wsi.sh

Web application

You could use command line interface to run Web application.

# Run main server
lexsubgen-app run --host HOST 
                  --port PORT 
                  [--model-configs CONFIGS] 
                  [--start-ids START-IDS] 
                  [--start-all] 
                  [--restore-session]

Example:

# Run server and serve models BERT and XLNet. 
# For BERT create server for serving model and substitute generator instantly (load resources in memory).
# For XLNet create only server.
lexsubgen-app run --host '0.0.0.0' 
                  --port 5000 
                  --model-configs '["my_cool_configs/bert.jsonnet", "my_awesome_configs/xlnet.jsonnet"]' 
                  --start-ids '[0]'

# After shutting down server JSON file with session dumps in the '~/.cache/lexsubgen/app_session.json'.
# The content of this file looks like:
# [
#     'my_cool_configs/bert.jsonnet',
#     'my_awesome_configs/xlnet.jsonnet',
# ]
# You can restore it with flag 'restore-session'
lexsubgen-app run --host '0.0.0.0' 
                  --port 5000 
                  --restore-session
# BERT and XLNet restored now
Arguments:
Argument Default Description
--help Show this help message and exit
--host IP address of running server host
--port 5000 Port for starting the server
--model-configs [] List of file paths to the model configs.
--start-ids [] Zero-based indices of served models for which substitute generators will be created
--start-all False Whether to create substitute generators for all served models
--restore-session False Whether to restore session from previous Web application run

FAQ

  1. How to use gpu? - You can use environment variable CUDA_VISIBLE_DEVICES to use gpu for inference: export CUDA_VISIBLE_DEVICES='1' or CUDA_VISIBLE_DEVICES='1' before your command.
  2. How to run tests? - You can use pytest: pytest tests
Owner
Samsung
Samsung Electronics Co.,Ltd.
Samsung
GitHub Repository
Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Photo-Realistic-Super-Resoluton Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" [Paper]

Harry Yang 199 Dec 01, 2022
Learned model to estimate number of distinct values (NDV) of a population using a small sample.

Learned NDV estimator Learned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maxim

2 Nov 21, 2022
Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Low-light Image Enhancement via Breaking Down the Darkness by Qiming Hu, Xiaojie Guo. 1. Dependencies Python3 PyTorch=1.0 OpenCV-Python, TensorboardX

Qiming Hu 30 Jan 01, 2023
Automatic Video Captioning Evaluation Metric --- EMScore

Automatic Video Captioning Evaluation Metric --- EMScore Overview For an illustration, EMScore can be computed as: Installation modify the encode_text

Yaya Shi 17 Nov 28, 2022
HyperDict - Self linked dictionary in Python

Hyper Dictionary Advanced python dictionary(hash-table), which can link it-self

8 Feb 06, 2022
This repository contains the official MATLAB implementation of the TDA method for reverse image filtering

ReverseFilter TDA This repository contains the official MATLAB implementation of the TDA method for reverse image filtering proposed in the paper: "Re

Fergaletto 2 Dec 13, 2021
💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

A 3D Generative Model for Structure-Based Drug Design Coming soon... Citation @inproceedings{luo2021sbdd, title={A 3D Generative Model for Structu

Shitong Luo 118 Jan 05, 2023
Dynamic Realtime Animation Control

Our project is targeted at making an application that dynamically detects the user’s expressions and gestures and projects it onto an animation software which then renders a 2D/3D animation realtime

Harsh Avinash 10 Aug 01, 2022
Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

SentiBERT Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020). https://arxiv.org/abs/20

Da Yin 66 Aug 13, 2022
[AAAI 2021] MVFNet: Multi-View Fusion Network for Efficient Video Recognition

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021) Overview We release the code of the MVFNet (Multi-View Fusion Network).

Wenhao Wu 114 Nov 27, 2022
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

61 Jan 07, 2023
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

LiDAR fog simulation Created by Martin Hahner at the Computer Vision Lab of ETH Zurich. This is the official code release of the paper Fog Simulation

Martin Hahner 110 Dec 30, 2022
Ensembling Off-the-shelf Models for GAN Training

Vision-aided GAN video (3m) | website | paper Can the collective knowledge from a large bank of pretrained vision models be leveraged to improve GAN t

345 Dec 28, 2022
Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Python-for-Epidemiologists This repository is an introduction to epidemiology analyses in Python. Additionally, the tutorials for my library zEpid are

Paul Zivich 120 Nov 17, 2022
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
learned_optimization: Training and evaluating learned optimizers in JAX

learned_optimization: Training and evaluating learned optimizers in JAX learned_optimization is a research codebase for training learned optimizers. I

Google 533 Dec 30, 2022
Pytorch implementation of the paper Time-series Generative Adversarial Networks

TimeGAN-pytorch Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19. Jinsung Yoon, Daniel Jarrett

Zhiwei ZHANG 21 Nov 24, 2022
Voice assistant - Voice assistant with python

🌐 Python Voice Assistant 🌵 - User's greeting 🌵 - Writing tasks to todo-list ?

PythonToday 10 Dec 26, 2022