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
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

Amazon 101 Dec 29, 2022
Let's Git - Versionsverwaltung & Open Source Hausaufgabe

Let's Git - Versionsverwaltung & Open Source Hausaufgabe Herzlich Willkommen zu dieser Hausaufgabe für unseren MOOC: Let's Git! Wir hoffen, dass Du vi

1 Dec 13, 2021
Parallel and High-Fidelity Text-to-Lip Generation; AAAI 2022 ; Official code

Parallel and High-Fidelity Text-to-Lip Generation This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose P

Zhying 77 Dec 21, 2022
Rocket-recycling with Reinforcement Learning

Rocket-recycling with Reinforcement Learning Developed by: Zhengxia Zou I have long been fascinated by the recovery process of SpaceX rockets. In this

Zhengxia Zou 202 Jan 03, 2023
PyTorch reimplementation of minimal-hand (CVPR2020)

Minimal Hand Pytorch Unofficial PyTorch reimplementation of minimal-hand (CVPR2020). you can also find in youtube or bilibili bare hand youtube or bil

Hao Meng 228 Dec 29, 2022
Custom studies about block sparse attention.

Block Sparse Attention 研究总结 本人近半年来对Block Sparse Attention（块稀疏注意力）的研究总结（持续更新中）。按时间顺序，主要分为如下三部分： PyTorch 自定义 CUDA 算子——以矩阵乘法为例 基于 Triton 的 Block Sparse A

Chen Kai 2 Jan 09, 2022
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data arXiv This is the code base for weakly supervised NER. We provide a

Amazon 92 Jan 04, 2023
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
Transformer in Vision

Transformer-in-Vision Recent Transformer-based CV and related works. Welcome to comment/contribute! Keep updated. Resource SCENIC: A JAX Library for C

Yong-Lu Li 1.1k Dec 30, 2022
Best practices for segmentation of the corporate network of any company

Best-practice-for-network-segmentation What is this? This project was created to publish the best practices for segmentation of the corporate network

2k Jan 07, 2023
Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models

Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models. You can easily generate all kind of art from drawing, painting, sketch, or even a specific artist style just using a t

Muhammad Fathy Rashad 643 Dec 30, 2022
Efficient and intelligent interactive segmentation annotation software

Efficient and intelligent interactive segmentation annotation software

294 Dec 30, 2022
Cross View SLAM

Cross View SLAM This is the associated code and dataset repository for our paper I. D. Miller et al., "Any Way You Look at It: Semantic Crossview Loca

Ian D. Miller 99 Dec 09, 2022
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

This is the original implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.1

Zhengyao Jiang 1.5k Dec 29, 2022
An image processing project uses Viola-jones technique to detect faces and then use SIFT algorithm for recognition.

Attendance_System An image processing project uses Viola-jones technique to detect faces and then use LPB algorithm for recognition. Face Detection Us

8 Jan 11, 2022
This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language Models"

GreaseLM: Graph REASoning Enhanced Language Models This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language

137 Jan 02, 2023
Individual Treatment Effect Estimation

CAPE Individual Treatment Effect Estimation Run CAPE python train_causal.py --loop 10 -m cape_cau -d NI --i_t 1 Run a baseline model python train_cau

S. Deng 4 Sep 02, 2022
Aircraft design optimization made fast through modern automatic differentiation

Aircraft design optimization made fast through modern automatic differentiation. Plug-and-play analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

Peter Sharpe 394 Dec 23, 2022
Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Imbalanced Continual Learning with Partioning Reservoir Sampling This repository contains the official PyTorch implementation and the dataset for our

Chris Dongjoo Kim 40 Sep 18, 2022
Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

HAIS Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021) by Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang*. (*) Corresp

Hust Visual Learning Team 145 Jan 05, 2023