This repository contains the instructions to reproduce the experiments in the GeneSis paper, accepted at EMNLP 2021. When using this work, please cite it as follows:
@inproceedings{lacerraetal:2021,
title={ Gene{S}is: {A} {G}enerative {A}pproach to {S}ubstitutes in {C}ontext},
author={Lacerra, Caterina and Tripodi, Rocco and Navigli, Roberto},
booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
publisher={Association for Computational Linguistics},
year={2021},
address={Punta Cana, Dominican Republic}
}
a) Create and activate a new conda environment with
conda create -n genesis python=3.8
conda activate genesis
b) Install pythorch (code tested with version 1.11.0) following the instructions at https://pytorch.org/
c) Install the requirements with
pip install -r requirements.txt
If you want to experiment with the datasets generated from SemCor, download the generated datasets and put the files under the data/ directory.
Note that the name of each file has the following format: semcor_{similarity_threshold}_{split_size}_train.tsv. The dataset without {split_size} is the whole dataset.
If you want to test one of the models described in the paper, download the checkpoint and move the external folder under the output/ directory.
The structure of each output subfolder is the following:
|--- bart_{seed}_pt_{training_dataset}_drop_{dropout}_enc_lyd_{encoder_layerdropout}_dec_lyd_{decoder_layerdropout} \
|--- beams_15_return_3
|--- checkpoints
| |--- best_checkpoint.ckpt
|--- input_training_dataset.tsv # dataset used at training time
Setup the config file config/train.yml following to the comments.
Train the model with PYTHONPATH=$(pwd) python src/train.py --config_path config/train.yml.
You can optionally pass the following parameters:
--dropout # a new value for dropout, will override the one defined in config/train.yml
--encoder_layerdropout # a new value for encoder layer dropout, will override the on in config/train.yml
--decoder_layerdropout # a new value for decoder layer dropout, will override the on in config/train.yml
--seed # value for the seed. The default one is 0.
--ckpt # path to a checkpoint of a pre-trained model. It can be given as a parameter in order to continue training on a different dataset, defined in the 'finetune' field of the config/train.yml
The checkpoints for each run will be saved in the folder
output/ bart_{seed}_pt_{training_dataset}_drop_{dropout}_enc_lyd_{encoder_layerdropout}_dec_lyd_{decoder_layerdropout}/beams_{beam_size}_return_{return_sequences}/checkpoints/
To test a trained model on the lexical substitution task, run
PYTHONPATH=$(pwd) python src/test.py --config_path config/train.yml --ckpt path_to_checkpoint --cvp vocab/wordnet_vocab --cut_vocab --suffix ''
There are several parameters that can be defined:
--ckpt # path to the checkpoint to test. It is REQUIRED.
--suffix # suffix string for output file names. It is REQUIRED.
--cvp # path to the vocabulary to use. It is a REQUIRED parameter (give '' for testing without cutting on vocab)
--test # flag to test on test. If it is not given as a parameter, the model will be EVALUATED ON THE DEV SET!
--beams # beam size
--sequences # number of returned beams
--cut_vocab # flag to cut the output on a reduced vocabulary
--backoff # flag to use the backoff strategy
--baseline # flag to compute the baseline
--embedder # name of HuggingFace model to be used as embedder for computing contextualized representations used for ranking
--cuda_device # GPU id. If it's not given, the model will be tested on CPU
--gpus # number of GPUs to use. Required if --cuda_device is set to a GPU idThe test script will produce several output files in the output/ bart_{seed}_pt_{training_dataset}_drop_{dropout}_enc_lyd_{encoder_layerdropout}_dec_lyd_{decoder_layerdropout}/beams_{beam_size}_return_{return_sequences}/output_files/ folder.
The most important one is named output_{suffix}_{test_dataset_name}.txt and contains the raw (without cut on the datest, without backoff strategy), formatted, for each instance, as follows:
target_word.POS instance_id [target_indexes] # ex: rest.NOUN 1922 [1]
target_context # ex: the rest is up to you .
gold_substitutes # ex: #gold: remainder: 5.0 balance: 1.0
sequence 1 # ex: remainder, remainder of the work, the rest of it, balance, extra,
sequence 2 ...
sequence returned_sequences # ex: remainder, remainder of the work, the rest of it, the balance, extraThis file is used to clean the output and postprocess it before feeding it to the scorer, called by test.py.
Thus, if you have already computed this file we can directly evalute the trained model (for example, trying with or without the output vocabulary) without testing it again, as described in the next section.
Two other output files are {test_dataset}_cut_per_target_{suffix}_best.txt and {test_dataset}_cut_per_target_{suffix}_oot.txt, that format the output as required from the Perl scorer for the task evaluation, and finally {test_dataset}_cut_per_target_hr_{suffix}_output.txt that contains a more readable version of the model output, formatted as follows:
target_word.POS instance_id [target_indexes] # ex: rest.NOUN 1922 [1]
target_context # ex: the rest is up to you .
gold_substitutes # ex: #gold: remainder balance
collected_generations # ex: #generated: remainder, balance, extra, other, the, all
clean_output # ex: #clean: remainder: 0.88, balance: 0.75, rest: 1.0, whole_rest: 0.83, remnant: 0.79 ...The clean_output row contains the output of the model after the vocab cut (if --cut_vocab is specified) and with fallback strategy (if --backoff). The floats are the cosine similarities between target and substitute.
It is possible to try different configurations of a model already tested by running PYTHONPATH=$(pwd) python src/task_evaluation.py --config_path config/train.yml. The parameters are the same of test.py, plus --finetune that has to be set if you trained your model with pre-training + finetuning. Note that the only parameters that can be changed without re-testing the model are --cvp, --cut_vocab and --backoff.
In order to generate new silver data, is it possible to use the scripts in the src/dataset_generation folder.
a) annotate_semcor.py builds an input file properly formatted for the model, starting from a pair of files in the Raganato framework's format
b) The generated file is the input required by generate_dataset.py, to be provided through the --sentences_path parameter. In this case, the config file to give as parameter is config/dataset_generation.yml. All the other parameters have been discussed in the sections above.
c) dataset_cleaning.py cleans out the generated dataset. The --threshold parameter is the threshold on cosine similarity between target and substitutes.
d) Finally, semcor_splits.py produces a split of the clean dataset. The parameters are commented in the code.
The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 under the European Union’s Horizon 2020 research and innovation programme.
This work was supported in part by the MIUR under grant “Dipartimenti di eccellenza 2018-2022” of the Department of Computer Science of the Sapienza University of Rome.
This work is under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license