Code for Towards Semantics-Enhanced Pre-Training: Can Lexicon Definitions Help Learning Sentence Meanings? (AAAI 2021)
Self-supervised pre-training techniques, albeit relying on large amounts of text, have enabled rapid growth in learning language representations for natural language understanding. However, as radically empirical models on sentences, they are subject to the input data distribution, inevitably incorporating data bias and reporting bias, which may lead to inaccurate understanding of sentences. To address this problem, we propose to adopt a human learner’s approach: when we cannot make sense of a word in a sentence, we often consult the dictionary for specific meanings; but can the same work for empirical models? In this work, we try to inform the pre-trained masked language models of word meanings for semantics-enhanced pre-training. To achieve a contrastive and holistic view of word meanings, a definition pair of two related words is presented to the masked language model such that the model can better associate a word with its crucial semantic features. Both intrinsic and extrinsic evaluations validate the proposed approach on semantics-orientated tasks, with an almost negligible increase of training data.
A preliminary version of the paper can be found here. The appendix can be found here.
RoBERTa-large
RoBERTa-large with SemPre
- Ubuntu 18.04
- python 3.6
- pytorch 1.3.1 + cudatoolkit 10.0
- fairseq 0.9.0
- nltk
- NVIDIA apex
We run the experiments on NVIDIA GPUs. conda is recommended. NOTE: the codes have been re-organized quite a bit. Especially, we monkey patch fairseq to backport some functionalities. If you encounter problems, please contact the authors.
To replicate the results:
conda create -n sempre python=3.6 pytorch=1.3.1 cudatoolkit=10.0 -c pytorch
conda activate sempre
conda install nltk
# some of fairseq's dependencies can be installed by conda:
# conda install requests cython future portalocker=2.0
# install NVIDIA apex using the pre-complied version
pip install ./scripts/utils/apex-0.1-cp36-cp36m-linux_x86_64.whl
pip install fairseq==0.9.0
For training, we extract wordnet definitions using nltk and you may need other data for MLM validation, which is optional though. If you have never used WordNet related functions in NLTK, you need to download it first. It will be prompt by nltk, please follow the given instructions or:
conda activate sempre
python -c "import nltk; nltk.download('wordnet')"
For validation, we use WiC, PIQA, CATs, which can be obtained from the corresponding links. WGs can be processed using scripts at here.
The given scripts supposed using conda with an environment named sempre.
We use pre-trained RoBERTa-{base,large} which can be downloaded here. Please extract the content from the archive and place the model checkpoint model.pt under scripts/checkpoints/roberta.{base,large}.
Running SemPre on RoBERTa-large with 4 Titan RTX:
cd scripts
MODEL=roberta.large/model # path for downloaded .pt checkpoint
DATA_ROOT=data-bin/ # where the data is
DATA=data # a folder with dict.txt (required), label_dict.txt (required),
# valid.bin (optional), and valid.idx (optional)
# valid.bin and valid.idx is needed only if MLM validation is needed,
# and they should be preprocessed using GPT2-BPE and dict.txt
# tune file-max for multi-GPU training
# ulimit -n 4096
# using the following command, the checkpoints will be named as $MODEL/batchsize2048-seed1234/checkpoint{1...10}.pt
GPU=0,1,2,3 NUM_GPU=4 ARGS="--fp16 --fp16-init-scale 1" MODEL=$MODEL DATA_ROOT=$DATA_ROOT DATA=$DATA ARCH=roberta_large MAX_EPOCH=10 WARMUP_UPDATES=295 MAX_SENTENCES=4 UPDATE_FREQ=128 TOKENS_PER_SAMPLE=256 MAX_POSITIONS=256 USE_LAMB=1 bash run-sempre_full-roberta.sh
RoBERTa-base should run fine with 1 2080 Ti. For other usages, please see run-sempre_{full|partial|simple}.sh.
The instructions are provided in the corresponding scripts. To replicate the results, please use the SemPre checkpoints provided below. Place the checkpoints as scripts/checkpoints/roberta.{base,large}/model/batchsize2048-seed1234/checkpoint1.pt:
- WGs:
word_guess.py - CATs:
making_sense.py - WiC:
run-wic.sh
Please download the data in jsonl from here. Place the jsonl files underscripts/data-raw/WiC. To replicate the results, run the following with a 2080Ti GPU.cd scripts MODEL=checkpoints/roberta.large/model/batchsize2048-seed1234/checkpoint1 DATA_ROOT=data-raw/WiC/ # the script defaults to the best hyperparameters and will run validation and conduct # inference on test. The best checkpoint, logs, and the test results will be # written to a subfolder named WiC-batchsize32-lr1e-5-seed5-me50 GPU=0 ARGS="--fp16 --fp16-init-scale 1" MODEL=$MODEL bash run-wic.sh - PIQA:
run-piqa.shPlease download the data from here (merge the .lst to the .jsonl as a field named label). Place the jsonl files underscripts/data-raw/PIQA. To replicate the results, run the following with a 2080Ti GPU.cd scripts MODEL=checkpoints/roberta.large/model/batchsize2048-seed1234/checkpoint1 DATA_ROOT=data-raw/PIQA/ # the script defaults to the best hyperparameters and will run validation and conduct # inference on test. The best checkpoint, logs, and the test results will be # written to a subfolder named PIQA-batchsize32-lr1e-5-seed9100-me50 GPU=0 ARGS="--fp16 --fp16-init-scale 1" MODEL=$MODEL bash run-piqa.sh
Trained RoBERTa-base and RoBERTa-large with SemPre can be found here. Best-performing validation logs for WiC and PIQA can also found there.
If you find the paper and the code helpful to your research, please cite as:
@inproceedings{ren2021towards,
author = {Xuancheng Ren and
Xu Sun and
Houfeng Wang and
Qun Liu},
title = {Towards Semantics-Enhanced Pre-Training: Can Lexicon Definitions
Help Learning Sentence Meanings?},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2021}
}