Overlapping with language modelling and emotion detection

Pytorch implementation to reproduce experiments from "Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes" - (poster).

If you use this code or our results in your research, please cite as appropriate:

@inproceedings{kocher-etal-2019-alleviating,
    title = "Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes",
    author = "Kocher, No{\'e}mien  and
      Scuito, Christian  and
      Tarantino, Lorenzo  and
      Lazaridis, Alexandros  and
      Fischer, Andreas  and
      Musat, Claudiu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1083",
    doi = "10.18653/v1/K19-1083",
    pages = "890--899",
}

This repo holds experiments on 4 models using the "overlapping" method:

awd, AWD ASGD Weight-Dropped LSTM, (/awd)
text simple, a very basic lstm for language modelling, (/simple)
mos, MOS Mixture of Softmaxes, (/mos)
voice simple, a very basic LSTM for emotion detection on voice, (/emotions)

To specify which model to run, use --main-model {simple-lstm | awd-lstm | mos-lstm | emotions-simple-lstm} argument. There are additional common paramaters, as well as specific parameters for each model. Those can be found in main_run.py.

The taxonomy in the code may differe a bit from the paper, especially regarding the type of experiments. Here is the corresponding terms:

In the code	In the paper
No order	Extreme TOI
Local order	Inter-batch TOI
Standard order	Standard TOI
Total order (P)	Alleviated TOI (P)

Experiments were run on a Tesla P100 GPU. Results are very likely to differ based on the GPU used.

Set-up

Download the data (PTB, WT2, WT103):

chmod +x get_data.sh
./get_data.sh

For emotions, add in data/IEMOCAP/ the all_features_cv files.

We use python 3.6 with Pytorch 0.4.1. To create a new python environement and install dependencies, run:

python3 -m virtualenv venv
source venv/bin/activate
pip3 install -r requirements.txt

You can check your setup by launching a quick training over one epoch with the following command:

python3 main_run.py --main-model awd-lstm --batch-size 20 --data data/penn --epochs 1 --nhid 5 --emsize 5 --nlayers 1 --bptt 5

The program should exit without error and write the logs in the logs/ folder. You can watch the logs with tensorboard by launching the following command:

tensorboard --logdir logs/

About the files

main_run.py is the main entry point that parses arguments, does the global initialization and runs the corresponding model and task.

awd/, emotions/, mos/ and simple/ are the different models directories. common/ holds the common initilization and utilities, such as the different data iterators, which are in the DataSelector class in common/excavator.py.

The main_run.py file, after performing the common initilizations, imports the main.py file corresponding to the choosen model.

Commands to reproduce the experiments

Note: Those results do not use prime batch size, but the default parameters. To have better results, adapt the --batch-size param to the closest prime number.

Quick anchors navigation:

Model	Dataset	Experiments
AWD	PTB	Extreme / Inter-batch / Original / Alleviated TOI
	WT2	Extreme / Inter-batch / Original / Alleviated TOI
	WT103	Extreme / Inter-batch / Original / Alleviated TOI
Text simple LSTM	PTB	Extreme / Inter-batch / Original / Alleviated TOI
Text simple LSTM	WT2	Extreme / Inter-batch / Original / Alleviated TOI
MOS	PTB	Original / Alleviated TOI
Voice simple LSTM	IEMOCAP	Extreme / Inter-batch / Original / Alleviated TOI

AWD PTB

Extreme TOI:

Expected results: 66.38 / 63.49 (validation / testing)

python3 main_run.py --main-model awd-lstm --batch-size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --seed-shuffle 141 --epochs 1000 --shuffle-full-seq

Inter-batch TOI:

Expected results: 66.96 / 64.20 (validation / testing)

python3 main_run.py --main-model awd-lstm --batch-size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --seed-shuffle 141 --epochs 1000 --shuffle-row-seq

Standard TOI:

Expected results: 61.28 / 58.94 (validation / testing)

python3 main_run.py --main-model awd-lstm --batch-size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --epochs 1000

Alleviated TOI {2,5,7,10}:

Expected results (validation / testing):

2: 61.73 / 59.37
5: 63.37 / 60.50
7: 59.22 / 56.7
10: 68.09 / 65.88

overlaps=(2 5 7 10)
epochs=1000
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model awd-lstm --batch-size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --epochs "$(($epochs/$k))" --init-seq "overlapCN_${k}"
    sleep 10
done

💥 With a prime batch size:

Expected results (validation / testing):

2: 60.56 / 57.97
5: 59.52 / 57.14
7: 59.43 / 57.16
10: 58.96 / 56.46

overlaps=(2 5 7 10)
epochs=1000
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model awd-lstm --batch-size 19 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --epochs "$(($epochs/$k))" --init-seq "overlapCN_${k}"
    sleep 10
done

AWD WT2

Extreme TOI

Expected results: 77.14 / 73.52 (validation / testing)

python3 main_run.py --main-model awd-lstm --epochs 750 --data /data/noemien.kocher/datasets/wikitext-2 --dropouth 0.2 --seed 1882 --batch-size 80 --shuffle-full-seq

Inter-batch TOI

Expected results: 76.08 / 72.61 (validation / testing)

python main_run.py --main-model awd-lstm --epochs 750 --data /data/noemien.kocher/datasets/wikitext-2 --dropouth 0.2 --seed 1882 --batch-size 80 --shuffle-row-seq

Standard TOI

Expected results: 68.50 / 65.86 (validation / testing)

python3 main_run.py --main-model awd-lstm --epochs 750 --data /data/noemien.kocher/datasets/wikitext-2 --dropouth 0.2 --seed 1882 --batch-size 80

Alleviated TOI {2,5,7,10}

Expected results (validation / testing):

2: 68.56 / 65.51
5: 69.56 / 66.33
7: 67.48 / 64.87
10: 72.95 / 69.69

overlaps=(2 5 7 10)
epochs=750
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model awd-lstm --data /data/noemien.kocher/datasets/wikitext-2 --dropouth 0.2 --seed 1882 --batch-size 80 --epochs "$(($epochs/$k))" --init-seq "overlapCN_${k}"
    sleep 10
done

💥 With a prime batch size:

Expected results (validation / testing):

2: 68.11 / 65.14
5: 67.74 / 65.11
7: 67.79 / 64.79
10: 67.47 / 64.73

overlaps=(2 5 7 10)
epochs=750
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model awd-lstm --data /data/noemien.kocher/datasets/wikitext-2 --dropouth 0.2 --seed 1882 --batch-size 79 --epochs "$(($epochs/$k))" --init-seq "overlapCN_${k}"
    sleep 10
done

AWD WT103

Extreme TOI

Expected results: 35.22 / 36.19 (validation / testing)

python3 -u main_run.py --main-model awd-lstm --epochs 14 --nlayers 4 --emsize 400 --nhid 2500 --alpha 0 --beta 0 --dropoute 0 --dropouth 0.1 --dropouti 0.1 --dropout 0.1 --wdrop 0 --wdecay 0 --bptt 140 --batch-size 60 --optimizer adam --lr 1e-3 --data /data/noemien.kocher/datasets/wikitext-103 --when 12 --model QRNN --shuffle-full-seq

Inter-batch TOI

Expected results: 35.41 / 36.39 (validation / testing)

python3 -u main_run.py --main-model awd-lstm --epochs 14 --nlayers 4 --emsize 400 --nhid 2500 --alpha 0 --beta 0 --dropoute 0 --dropouth 0.1 --dropouti 0.1 --dropout 0.1 --wdrop 0 --wdecay 0 --bptt 140 --batch-size 60 --optimizer adam --lr 1e-3 --data /data/noemien.kocher/datasets/wikitext-103 --when 12 --model QRNN --shuffle-row-seq

Standard TOI

Expected results: 32.18 / 32.94 (validation / testing)

python3 -u main_run.py --main-model awd-lstm --epochs 14 --nlayers 4 --emsize 400 --nhid 2500 --alpha 0 --beta 0 --dropoute 0 --dropouth 0.1 --dropouti 0.1 --dropout 0.1 --wdrop 0 --wdecay 0 --bptt 140 --batch-size 60 --optimizer adam --lr 1e-3 --data /data/noemien.kocher/datasets/wikitext-103 --when 12 --model QRNN

Alleviated TOI {2,5,7,10}

Expected results (validation / testing):

2: 36.94 / 34.31
5: 38.50 / 40.04
7: 31.78 / 32.72
10: 48.28 / 49.49

# base num epochs is 14
overlaps=(2 5 7 10)
when_steps=147456
max_steps=172032
for i in "${!overlaps[@]}"
do
        :
        python3 -u main_run.py --main-model awd-lstm --epochs 14 --nlayers 4 --emsize 400 --nhid 2500 --alpha 0 --beta 0 --dropoute 0 --dropouth 0.1 --dropouti 0.1 --dropout 0.1 --wdrop 0 --wdecay 0 --bptt 140 --batch-size 60 --optimizer adam --lr 1e-3 --data /data/noemien.kocher/datasets/wikitext-103 --when-steps "$when_steps" --model QRNN --init-seq "overlapCN_${overlaps[$i]}" --log-dir /data/noemien.kocher/logs/ --max-steps "$max_steps"
        sleep 10
done

💥 With a prime batch size:

Expected results (validation / testing):

2: 32.00 / 32.98
5: 31.93 / 33.07
7: 31.78 / 32.89
10: 31.92 / 32.85

# base num epochs is 14
overlaps=(2 5 7 10)
when_steps=147456
max_steps=172032
for i in "${!overlaps[@]}"
do
        :
        python3 -u main_run.py --main-model awd-lstm --epochs 14 --nlayers 4 --emsize 400 --nhid 2500 --alpha 0 --beta 0 --dropoute 0 --dropouth 0.1 --dropouti 0.1 --dropout 0.1 --wdrop 0 --wdecay 0 --bptt 140 --batch-size 59 --optimizer adam --lr 1e-3 --data /data/noemien.kocher/datasets/wikitext-103 --when-steps "$when_steps" --model QRNN --init-seq "overlapCN_${overlaps[$i]}" --log-dir /data/noemien.kocher/logs/ --max-steps "$max_steps"
        sleep 10
done

Simple PTB

Extreme TOI:

Expected results: 81.97 / 79.08 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 20 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1500 --lr-decay 1 --shuffle-full-seq

Inter-batch TOI:

Expected results: 81.67 / 78.59 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 20 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1500 --lr-decay 1 --shuffle-row-seq

Standard TOI:

Expected results: 77.54 / 75.36 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 20 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1500 --lr-decay 1

Alleviated TOI {2,5,7,10}:

Expected results (validation / testing):

2: 78.48 / 76.55
5: 91.95 / 89.64
7: 77.47 / 74.98
10: 92.92 / 92.07

overlaps=(2 5 7 10)
epochs=100
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model simple-lstm --epochs "$(($epochs/$k))" --batch-size 20 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1500 --lr-decay 1 --init-seq "overlapCN_${k}"
    sleep 10
done

Simple WT2

Extreme TOI

Expected results: 101.3 / 96.08 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 80 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1150 --lr-decay 1 --data /data/noemien.kocher/datasets/wikitext-2 --shuffle-full-seq

Inter-batch TOI

Expected results: 101.7 / 96.89 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 80 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1150 --lr-decay 1 --data /data/noemien.kocher/datasets/wikitext-2 --shuffle-row-seq

Standard TOI

Expected results: 98.85 / 93.15 (validation / testing)

python3 main_run.py --main-model simple-lstm --epochs 100 --batch-size 80 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1150 --lr-decay 1 --data /data/noemien.kocher/datasets/wikitext-2

Alleviated TOI {2,5,7,10}

Expected results (validation / testing):

2: 100.4 / 94.49
5: 113.5 / 106.1
7: 98.25 / 92.77
10: 151.0 / 135.1

overlaps=(2 5 7 10)
epochs=100
for k in "${overlaps[@]}"
do
    :
    python3 main_run.py --main-model simple-lstm --epochs "$(($epochs/$k))" --batch-size 80 --dropout 0.15 --nlayers 2 --bptt 70 --nhid 1150 --lr-decay 1 --data /data/noemien.kocher/datasets/wikitext-2 --init-seq "overlapCN_${k}"
    sleep 10
done

MOS PTB

Standard TOI:

Expected results: 58.49 / 56.19 (validation / testing)

python3 main_run.py --main-model mos-lstm --data data/penn --dropouti 0.4 --dropoutl 0.29 --dropouth 0.225 --seed 28 --batch-size 12 --lr 20.0 --epochs 1000 --nhid 960 --nhidlast 620 --emsize 280 --n-experts 15

Alleviated TOI {1..40}:

💥 With a prime batch size:

epochs=2000
for k in {1..70}
do
        :
        python3 main_run.py --main-model mos-lstm --data data/penn --dropouti 0.4 --dropoutl 0.29 --dropouth 0.225 --seed 28 --batch-size 13 --lr 20.0 --epochs "$(($epochs/$k))" --nhid 960 --nhidlast 620 --emsize 280 --n-experts 15 --init-seq "overlapCNF_${k}"
        sleep 10
done

Expected results (validation / testing):

1: 58.36 / 56.21
2: 58.07 / 55.76
3: 58.03 / 55.79
4: 52.82 / 55.63
5: 57.81 / 55.63
6: 57.55 / 55.32
7: 57.47 / 55.23
8: 57.47 / 55.34
9: 57.16 / 54.93
10: 57.34 / 54.90
11: 57.11 / 54.98
12: 57.47 / 55.44
13: 67.77 / 66.01
14: 56.76 / 54.58 (paper's result)
15: 57.44 / 55.20
16: 56.95 / 54.86
17: 57.64 / 55.14
18: 57.38 / 54.93
19: 57.55 / 55.35
20: 57.00 / 54.67
21: 57.55 / 55.22
22: 57.54 / 55.19
23: 57.29 / 54.90
24: 57.47 / 55.11
25: 57.12 / 54.85
26: 66.14 / 63.81
27: 57.08 / 54.85
28: --.-- / --.--
29: --.-- / --.--
30: --.-- / --.--
31: 57.74 / 55.37
32: 57.21 / 55.26
33: 57.66 / 55.40
34: 57.48 / 55.44
35: 56.44 / 54.33 (post-result, not in the paper)
36: 57.10 / 55.09
37: 57.55 / 55.29
38: 57.04 / 54.87
39: 64.37 / 62.54
40: 57.52 / 54.99

Voice simple LSTM

Extreme TOI:

Expected result: 0.475 / 0.377 (WA / UA)

python3 main_run.py --main-model emotions-simple-lstm --cv 5 --data data/IEMOCAP/all_features_cv --test-batch-size 20 --lr 0.05 --log-interval 20 --lr-decay 1 --step-size 0.1 --epochs 60 --order complete_random

Inter-batch TOI:

Expected result: 0.478 / 0.386 (WA / UA)

python3 main_run.py --main-model emotions-simple-lstm --cv 5 --data data/IEMOCAP/all_features_cv --test-batch-size 20 --lr 0.05 --log-interval 20 --lr-decay 1 --step-size 0.1 --epochs 60 --window-size 300 --order local_order

Standard TOI:

Expected result: 0.486 / 0.404 (WA / UA)

python3 main_run.py --main-model emotions-simple-lstm --cv 5 --data data/IEMOCAP/all_features_cv --test-batch-size 20 --lr 0.05 --log-interval 20 --lr-decay 1 --step-size 0.1 --epochs 60 --order standard_order

Alleviated TOI 10:

Expected result:

15k steps: 0.553 / 0.489 (WA / UA)
60 epochs: 0.591 / 0.523 (WA / UA)

python3 main_run.py --main-model emotions-simple-lstm --cv 5 --data data/IEMOCAP/all_features_cv --test-batch-size 20 --lr 0.05 --log-interval 20 --lr-decay 1 --step-size 0.1 --epochs 60 --order total_order

Delayed-reset standard TOI {1,2,5,7,10} with PTB

Expected results (validation / testing):

1: 61.28 / 58.94
2: 60.76 / 58.55
5: 60.10 / 57.83
7: 60.08 / 57.76
10: 60.05 / 57.78

P=(1 2 5 7 10)
epochs=1000
for k in "${P[@]}"
do
    :
    python3 main_run.py --main-model awd-lstm-repetitions --batch-size 20 --data data/penn --dropouti 0.4 --dropouth 0.25 --seed 141 --epochs 1000 --use-repetitions "${k}"
    sleep 10
done

Acknowledgements

Code is heavily borrowed from the following sources:

simple-lstm (simple/): https://github.com/deeplearningathome/pytorch-language-model
awd-lstm (awd/): https://github.com/salesforce/awd-lstm-lm
mos-lstm: (mos/) https://github.com/zihangdai/mos

Name		Name	Last commit message	Last commit date
Latest commit History 139 Commits
awd		awd
common		common
docker		docker
emotions		emotions
mos		mos
simple		simple
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
get_data.sh		get_data.sh
main_run.py		main_run.py
poster-conll.pdf		poster-conll.pdf
requirements.txt		requirements.txt

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Overlapping with language modelling and emotion detection

Set-up

About the files

Commands to reproduce the experiments

AWD PTB

AWD WT2

AWD WT103

Simple PTB

Simple WT2

MOS PTB

Voice simple LSTM

Delayed-reset standard TOI {1,2,5,7,10} with PTB

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Overlapping with language modelling and emotion detection

Set-up

About the files

Commands to reproduce the experiments

AWD PTB

AWD WT2

AWD WT103

Simple PTB

Simple WT2

MOS PTB

Voice simple LSTM

Delayed-reset standard TOI {1,2,5,7,10} with PTB

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