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# Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
This page includes instructions for reproducing results from the paper [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](https://arxiv.org/abs/1902.07816).
## Download data
First, follow the [instructions to download and preprocess the WMT'17 En-De dataset](../translation#prepare-wmt14en2desh).
Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`.
## Train a model
Then we can train a mixture of experts model using the `translation_moe` task.
Use the `--method` flag to choose the MoE variant; we support hard mixtures with a learned or uniform prior (`--method hMoElp` and `hMoEup`, respectively) and soft mixures (`--method sMoElp` and `sMoEup`).
The model is trained with online responsibility assignment and shared parameterization.
The following command will train a `hMoElp` model with `3` experts:
```bash
fairseq-train --ddp-backend='legacy_ddp' \
data-bin/wmt17_en_de \
--max-update 100000 \
--task translation_moe --user-dir examples/translation_moe/translation_moe_src \
--method hMoElp --mean-pool-gating-network \
--num-experts 3 \
--arch transformer_wmt_en_de --share-all-embeddings \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \
--lr 0.0007 \
--dropout 0.1 --weight-decay 0.0 --criterion cross_entropy \
--max-tokens 3584
```
## Translate
Once a model is trained, we can generate translations from different experts using the `--gen-expert` option.
For example, to generate from expert 0:
```bash
fairseq-generate data-bin/wmt17_en_de \
--path checkpoints/checkpoint_best.pt \
--beam 1 --remove-bpe \
--task translation_moe --user-dir examples/translation_moe/translation_moe_src \
--method hMoElp --mean-pool-gating-network \
--num-experts 3 \
--gen-expert 0
```
## Evaluate
First download a tokenized version of the WMT'14 En-De test set with multiple references:
```bash
wget dl.fbaipublicfiles.com/fairseq/data/wmt14-en-de.extra_refs.tok
```
Next apply BPE on the fly and run generation for each expert:
```bash
BPE_CODE=examples/translation/wmt17_en_de/code
for EXPERT in $(seq 0 2); do \
cat wmt14-en-de.extra_refs.tok \
| grep ^S | cut -f 2 \
| fairseq-interactive data-bin/wmt17_en_de \
--path checkpoints/checkpoint_best.pt \
--beam 1 \
--bpe subword_nmt --bpe-codes $BPE_CODE \
--buffer-size 500 --max-tokens 6000 \
--task translation_moe --user-dir examples/translation_moe/translation_moe_src \
--method hMoElp --mean-pool-gating-network \
--num-experts 3 \
--gen-expert $EXPERT ; \
done > wmt14-en-de.extra_refs.tok.gen.3experts
```
Finally use `score_moe.py` to compute pairwise BLUE and average oracle BLEU:
```bash
python examples/translation_moe/score.py --sys wmt14-en-de.extra_refs.tok.gen.3experts --ref wmt14-en-de.extra_refs.tok
# pairwise BLEU: 48.26
# #refs covered: 2.11
# multi-reference BLEU (leave-one-out): 59.46
```
This matches row 3 from Table 7 in the paper.
## Citation
```bibtex
@article{shen2019mixture,
title = {Mixture Models for Diverse Machine Translation: Tricks of the Trade},
author = {Tianxiao Shen and Myle Ott and Michael Auli and Marc'Aurelio Ranzato},
journal = {International Conference on Machine Learning},
year = 2019,
}
```
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