Preliminary conclusions

Ensembling

Ensembling helps in the following ways. To summarize: use method 2 for speed or method 3 for accuracy.

1. A proper ensemble of the last 3 savepoints (1 hour interval) gives about 1 BLEU/chrF3 point.
2. An averaging "ensemble" (just average model parameters) of the last 3 savepoints gives roughly the same result as a proper ensemble.
3. A proper ensemble of 3 independently initialized and trained models is about 2 BLEU/chrF3 points above the baseline, and 1 point above the ensemble models above.

Various notes

• Word-based decoders are pretty bad in both directions. Why?
• Layer Normalization seems to hurt.
• Dropout seems to hurt (Austin says this is particularly true for the decoder side).
• Attention loss at most helps a little bit, perhaps not at all.
• A large source vocabulary helps even with a hybrid encoder (Luong & Manning found the same thing)

newstest2015-enfi

Results are sorted by chrF3, this is the measure that correlates best with human judgement for English-Finnish according to the WMT shared task on evaluation metrics.

newstest2016-enfi

newstest2015-fien

Results are sorted by BLEU.

Details

The HNMT variants used above are:

• run21+22+24: ensemble between 3 independent models, these use Europarl + 1M of Turku's parallel sentences (1011 version, hopefully the final one) + 1M tokens of backtranslated news.
• run16+17+18: ensemble between 3 independent models, these use Europarl + 1M of Turku's parallel sentences (0811 version, not the final one). From this point, cased and untokenized corpora are used.
• ensemble4: 4 latest savepoints (3 hour intervals) ensembled
• average-ensemble4: 4 latest savepoints (3 hour intervals) model parameters averaged
• char: character-based decoder
• word: word-based encoder (no UNK replacement or anything, always 50k target vocabulary)
• long: sentence length limit (longer sentences are removed entirely): 60 words/360 chars
• short: 30 words/180 chars
• ln: layer normalization is used
• dropout: dropout factor 0.2 is used (default is no dropout)
• svocXXX: size of source vocabulary (always with character backoff, default is 10k)
• alignXXX:YYY: attention loss is used, initial value XXX with exponential decay factor YYY per batch

Other parameter values:

• lowercase yes (on both sides, trained on lowercased + tokenized data)
• 512 dim output LSTM for character-based models, 256 for word based
• 256 dim input LSTM
• 256 dim attention hidden layer
• batch size 128

Example SLURM script (from run16)

#!/bin/bash -l

#SBATCH -J hnmt
#SBATCH -o hnmt.stdout.%j
#SBATCH -e hnmt.stderr.%j
#SBATCH -t 72:00:00
#SBATCH -N 1
#SBATCH -p gpu
#SBATCH --mem=16384
#SBATCH --gres=gpu:1
#SBATCH --constraint=k80
#SBATCH

module purge
module load python-env/3.4.1
module load cuda/8.0
module list

cd ${SLURM_SUBMIT_DIR:-.}
pwd

echo "Starting at `date`"

SOURCE="en"
TARGET="fi"

MODEL=/wrk/rostling/models/hnmt/run16-$SOURCE-$TARGET-70h

THEANO_FLAGS=optimizer=fast_run,device=gpu,floatX=float32 python3 \
    hnmt.py \
    --save-model "$MODEL".model \
    --log-file "$MODEL".log \
    --source /wrk/rostling/wmt16/ep-turku1m."$SOURCE" \
    --target /wrk/rostling/wmt16/ep-turku1m."$TARGET" \
    --beam-size 4 \
    --source-tokenizer word \
    --target-tokenizer char \
    --max-source-length 100 \
    --max-target-length 600 \
    --source-lowercase no \
    --target-lowercase no \
    --dropout 0 \
    --word-embedding-dims 256 \
    --char-embedding-dims 64 \
    --encoder-state-dims 256 \
    --decoder-state-dims 512 \
    --attention-dims 256 \
    --source-vocabulary 25000 \
    --min-char-count 2 \
    --batch-size 64 \
    --save-every 2500 \
    --test-every 50 \
    --translate-every 1000 \
    --training-time 71

echo "Finishing at `date`"