This is code repo for our AAAI ICWSM 2019 paper "Adapting Sequence to Sequence models for Text Normalization in Social Media", where we explore several Seq2Seq variations for normalizing social media text.
If you find this code, models or results useful, please cite us using the following bibTex:
@inproceedings{lourentzou2019adapting,
title={Adapting Sequence to Sequence models for Text Normalization in Social Media},
author={Lourentzou, Ismini and Manghnani, Kabir and Zhai, ChengXiang},
booktitle={International Conference on Web and Social Media},
year={2019},
organization={AAAI}
}
- torch==0.4.1
- python 2.7
mkdir dataset
cd dataset
wget https://github.com/noisy-text/noisy-text.github.io/raw/master/2015/files/lexnorm2015.tgz
tar -zxvf lexnorm2015.tgz
cp lexnorm2015/* .
rm -rf lexnorm2015 lexnorm2015.tgz
cd ..The hybrid model is a combination of two Seq2Seq models: a word-level one (S2S) and a secondary character-level trained on pairs of words (spelling with noise augmented data).
i) Train a word-level model, save results in folder word_model
python main.py -logfolder -save_dir word_model -gpu 0 -input word -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -dropout 0.5 -emb_size 100 -end_epoch 50 -layers 3 -learning_rate_decay 0.05 -lr 0.01 -max_grad_norm 5 -rnn_size 200 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 15 -teacher_forcing_ratio 0.6 -max_train_decode_len 50ii) Train a secondary character-level model, save results in folder spelling_model
python main.py -logfolder -save_dir spelling_model -gpu 0 -input spelling -data_augm -noise_ratio 0.1 -attention -bias -lowercase -bos -eos -brnn -batch_size 500 -dropout 0.5 -emb_size 256 -end_epoch 50 -layers 3 -learning_rate_decay 0.05 -lr 0.001 -max_grad_norm 5 -rnn_size 500 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 30 -teacher_forcing_ratio 0.6 -max_train_decode_len 50Evaluate final model (HS2S) combining the trained models described above:
python main.py -eval -logfolder -save_dir hybrid_model -gpu 0 -load_from word_model/model_50_word.pt -char_model spelling_model/model_50_spelling.pt -input hybrid -data_augm -noise_ratio 0.1 -lowercase -bos -eos -batch_size 32 -share_vocabYou can also try some additional models, for example:
- Word-level model where tokens that need no normalization are mapped to a common @self token (S2SSelf)
python main.py -logfolder -save_dir S2SSelf -gpu 3 -self_tok -input word -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -dropout 0.2 -emb_size 100 -end_epoch 50 -layers 3 -learning_rate_decay 0.05 -lr 0.01 -max_grad_norm 10 -rnn_size 100 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 15 -teacher_forcing_ratio 0.6 -max_train_decode_len 50- Word-level model for non-unique mappings only (S2SMulti)
python main.py -logfolder -save_dir S2SMulti -gpu 4 -correct_unique_mappings -input word -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -dropout 0.5 -emb_size 100 -end_epoch 50 -layers 3 -learning_rate_decay 0.05 -lr 0.01 -max_grad_norm 5 -rnn_size 200 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 15 -teacher_forcing_ratio 0.6 -max_train_decode_len 50- Character-level model (S2SChar)
python main.py -logfolder -save_dir S2SChar -gpu 5 -input char -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -dropout 0.2 -emb_size 256 -end_epoch 50 -layers 3 -learning_rate_decay 0.5 -lr 0.001 -max_grad_norm 10 -rnn_size 512 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 30 -teacher_forcing_ratio 0.6 -max_train_decode_len 200We have done our best to ensure reproducibility of our results, however this is not always guaranteed.
As an extra reproducibility step, we also release our best performing models. Just unzip the pretrained_models.zip found here and try them by setting the flag eval and updating the model folders in your python commands, for example:
- Pre-trained hybrid model (HS2S):
python main.py -eval -logfolder -save_dir hybrid_model -gpu 0 -load_from pretrained_models/word_model/model_50_word.pt -char_model pretrained_models/spelling_model/model_50_spelling.pt -input hybrid -data_augm -noise_ratio 0.1 -lowercase -bos -eos -batch_size 32 -share_vocab- Pre-trained word-level model (S2S):
python main.py -eval -logfolder -gpu 0 -load_from pretrained_models/word_model/model_50_word.pt -input word -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -dropout 0.5 -emb_size 100 -end_epoch 50 -layers 3 -learning_rate_decay 0.05 -lr 0.01 -max_grad_norm 5 -rnn_size 200 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab -start_decay_after 15 -teacher_forcing_ratio 0.6 -max_train_decode_len 50A separate file pretrained_models/run.sh contains all commands for reproducing the aforementioned models.
With the interactive flag, you can also try the model on any arbitrary text through command line, for example:
foo@bar:~$ python main.py -interactive -gpu 0 -load_from pretrained_models/word_model/model_50_word.pt -char_model pretrained_models/spelling_model/model_50_spelling.pt -input hybrid -data_augm -noise_ratio 0.1 -attention -bias -lowercase -bos -eos -brnn -batch_size 32 -rnn_type 'LSTM' -tie_decoder_embeddings -share_embeddings -share_vocab
Please enter the text to be normalized (q to quit): lol how are u doin
Prediction is:laughing out loud how are you doing
Please enter the text to be normalized (q to quit): q
foo@bar:~$If you wish to work on CPU, simply remove the flag -gpu 0 from the following commands.
Each command prints in a file named output.log saved in defined by save_dir.
Remove the flag -logfolder to output to console.
Credits to khanhptnk and howardyclo as this project borrows code from their repositories.
Also, checkout MoNoise for a non-DL SoTA model.