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Direct Output Connection for a High-Rank Language Model

This repository contains source files we used in our paper

Direct Output Connection for a High-Rank Language Model

Sho Takase, Jun Suzuki, Masaaki Nagata

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Requirements

Python 3.5, PyTorch 0.2.0

About 9.5 GB of VRAM (tested on K80).

Download the data

./get_data.sh

Train the models (to reproduce our results)

Penn Treebank

First, train the model

python main.py --data data/penn --dropouti 0.4 --dropoutl 0.6 --dropouth 0.225 --seed 28 --batch_size 12 --lr 20.0 --epoch 500 --nhid 960 --nhidlast 620 --emsize 280 --n_experts 15 --num4second 5 --var 0.001 --nonmono 60 --save PTB --single_gpu

Second, finetune the model

python finetune.py --data data/penn --dropouti 0.4 --dropoutl 0.6 --dropouth 0.225 --seed 28 --batch_size 12 --lr 20.0 --epoch 500 --var 0.001 --nonmono 60 --save PATH_TO_FOLDER --single_gpu

where PATH_TO_FOLDER is the folder created by the first step (concatenation of PTB with a timestamp).

Third, run evaluation

python cal_ppl.py --data data/penn --save PATH_TO_FOLDER/finetune_model.pt --bptt 1000

WikiText-2 (Single GPU)

First, train the model

python main.py --epochs 500 --data data/wikitext-2 --save WT2 --dropouth 0.2 --seed 1882 --n_experts 15 --num4second 5 --var 0.001 --nhid 1150 --nhidlast 650 --emsize 300 --batch_size 15 --lr 15.0 --dropoutl 0.6 --small_batch_size 5 --max_seq_len_delta 20 --dropouti 0.55 --nonmono 60 --single_gpu

Second, finetune the model

python finetune.py --epochs 500 --data data/wikitext-2 --save PATH_TO_FOLDER --dropouth 0.2 --seed 1882 --var 0.001 --batch_size 15 --lr 15.0 --dropoutl 0.6 --small_batch_size 5 --max_seq_len_delta 20 --dropouti 0.55 --nonmono 60 --single_gpu

Third, run evaluation

python cal_ppl.py --data data/wikitext-2 --save PATH_TO_FOLDER/finetune_model.pt --bptt 1000

Pre-trained Models

https://drive.google.com/open?id=1ug-6ISrXHEGcWTk5KIw8Ojdjuww-i-Ci

ptb, wikitext2: models to obtain the single model results

ptb_ensemble, wikitext2_ensemble: other trained models to obtain ensemble results

Licenses

Files listed in NTT_LICENSE's EXHIBIT A are applied the NTT_LICENSE.

Other files are applied the LICENSE in this repository.

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