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lexsubgen's Introduction

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 P@1 P@3 R@10 GAP P@1 P@3 R@10
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

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