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Transformer-DyNet

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An Implementation of Transformer (Attention Is All You Need) in DyNet

Transformer-DyNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# An Implementation of [Transformer](http://papers.nips.cc/paper/7181-attention-is-all-you-need) in [DyNet](https://github.com/clab/dynet)

This project aims to develop a simplified, easy-to-use implementation of Transformer architecture. However, it still has all necessary functionalities to build a complete sequence to sequence system. Currently, Transformer-DyNet supports to build: 1) NMT models (or other sequence-to-sequence models); 2) purely-attentive LM.

### Dependencies

Before compiling dynet, you need:

* [Eigen](https://bitbucket.org/eigen/eigen), e.g. 3.3.x

* [cuda](https://developer.nvidia.com/cuda-toolkit) version 7.5 or higher

* [cmake](https://cmake.org/), e.g., 3.5.1 using *cmake* ubuntu package

### Building

First, clone the repository

git clone https://github.com/duyvuleo/Transformer-DyNet.git

As mentioned above, you'll need the latest [development] version of eigen

hg clone https://bitbucket.org/eigen/eigen/ (or latest stable version 3.3.4)

A modified version of latest [DyNet](https://github.com/clab/dynet) is already included (e.g., dynet folder). Current DyNet version: [v.2.0.3](https://github.com/clab/dynet/releases/tag/2.0.3).

#### CPU build

Compiling to execute on a CPU is as follows

mkdir build_cpu
    cd build_cpu
    cmake .. -DEIGEN3_INCLUDE_DIR=EIGEN_PATH
    make -j 2

MKL support. If you have Intel's MKL library installed on your machine, you can speed up the computation on the CPU by:

cmake .. -DEIGEN3_INCLUDE_DIR=EIGEN_PATH -DMKL=TRUE -DMKL_ROOT=MKL_PATH -DENABLE_BOOST=TRUE [-DBoost_NO_BOOST_CMAKE=ON]

substituting in different paths to EIGEN_PATH and MKL_PATH if you have placed them in different directories.

This will build the 3 binaries
    
    build_cpu/transformer-train
    build_cpu/transformer-decode
    build_cpu/transformer-lm

#### GPU build

Building on the GPU uses the Nvidia CUDA library, currently tested against version 7.5, 8.0, and 9.x.
The process is as follows

mkdir build_gpu
    cd build_gpu
    cmake .. -DBACKEND=cuda -DEIGEN3_INCLUDE_DIR=EIGEN_PATH -DCUDA_TOOLKIT_ROOT_DIR=CUDA_PATH -DCUDNN_ROOT=CUDA_PATH -DENABLE_BOOST=TRUE [-DBoost_NO_BOOST_CMAKE=ON]
    make -j 2

substituting in your EIGEN_PATH and CUDA_PATH folders, as appropriate.

For CUDA 9.x, you may encounter the following compiling error:

Eigen/Core:44:34: error: math_functions.hpp: No such file or directory

This may be a bug from Eigen. To resolve it, creating a symlink from cuda/include/math_functions.hpp to cuda/include/crt/math_functions.hpp:

ln -s /usr/local/cuda/include/crt/math_functions.hpp /usr/local/cuda/include/math_functions.hpp

This will result in the 3 binaries

build_gpu/transformer-train
    build_gpu/transformer-decode
    build_gpu/transformer-lm

In general, the programs built on GPU will run much faster than on CPU (even enhanced with MKL). However, GPU is limited to the memory (8-16Gb) whereas CPU is almost unlimited.

#### Using the model

The data can be processed by using the script (/scripts/wrap-data.py),

python scripts/wrap-data.py <src-lang-id> <trg-lang-id> <train-prefix> <dev-prefix> <test-prefix> <vocab-prefix>

(assume that 2 files vocab-prefix.src-lang-id and vocab-prefix.trg-lang-id must exist.)

python scripts/wrap-data.py <src-lang-id> <trg-lang-id> <train-prefix> <dev-prefix> <test-prefix> <src-word-freq-cutoff> <trg-word-freq-cutoff>

Example:

python scripts/wrap-data.py en vi sample-data/train.10k sample-data/test2012 sample-data/test2013 vocab

python scripts/wrap-data.py en vi sample-data/train.10k sample-data/test2012 sample-data/test2013 2 2

This script will create necessary data files (*.capped) that can be processed by transformer-train and transformer-decode.

First, print command line's help of transformer-train and transformer-decode (or transformer-lm),

./build_gpu/transformer-train --help
    ./build_gpu/transformer-decode --help
    ./build_gpu/transformer-lm --help

The model can be run as follows:

nice ./build_gpu/transformer-train --dynet-devices GPU:0 --max-seq-len 150 --minibatch-size 1024 --treport 1000 --dreport 20000 --src-vocab <your-path>/data/iwslt-envi/vocab.en --tgt-vocab experiments/data/iwslt-envi/vocab.vi -t <your-path>/data/iwslt-envi/train.en-vi.vcb.capped -d <your-path>/data/iwslt-envi/tst2012.en-vi.vcb.capped --model-path <your-path>/models/iwslt-envi -e 50 --lr-eta 0.1 --lr-patience 10 --patience 20 --lr-eta-decay 2 --encoder-emb-dropout-p 0.1 --encoder-sublayer-dropout-p 0.1 --decoder-emb-dropout-p 0.1 --decoder-sublayer-dropout-p 0.1 --attention-dropout-p 0.0 --ff-dropout-p 0.1 --ff-activation-type 1 --nlayers 2 --num-units 128 --num-heads 2 &><your-path>/models/iwslt-envi/log.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu &

or the vocabularies will be built on the fly:

nice ./build_gpu/transformer-train --dynet-devices GPU:0 --max-seq-len 300 --minibatch-size 1024  --treport 200 --dreport 20000 -t <your-path>/data/iwslt-envi/train.en-vi.vcb.capped -d <your-path>/data/iwslt-envi/tst2012.en-vi.vcb.capped --model-path <your-path>/models/iwslt-envi -e 50 --lr-eta 0.1 --lr-patience 10 --patience 20 --lr-eta-decay 2 --encoder-emb-dropout-p 0.1 --encoder-sublayer-dropout-p 0.1 --decoder-emb-dropout-p 0.1 --decoder-sublayer-dropout-p 0.1 --attention-dropout-p 0.0 --ff-dropout-p 0.1 --ff-activation-type 1 --nlayers 2 --num-units 128 --num-heads 2 &><your-path>/models/iwslt-envi/log.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu &

which will train a small model on a tiny training set, i.e.,

*** DyNet initialization ***
	[dynet] initializing CUDA
	[dynet] Request for 1 specific GPU ...
	[dynet] Device Number: 2
	[dynet]   Device name: GeForce GTX TITAN X
	[dynet]   Memory Clock Rate (KHz): 3505000
	[dynet]   Memory Bus Width (bits): 384
	[dynet]   Peak Memory Bandwidth (GB/s): 336.48
	[dynet]   Memory Free (GB): 12.6801/12.8002
	[dynet]
	[dynet] Device(s) selected: 2
	[dynet] random seed: 3052832559
	[dynet] allocating memory: 512MB
	[dynet] memory allocation done.

PID=27539
	Command: ./build_gpu/transformer-train --max-seq-len 300 --minibatch-size 1024 --treport 200 --dreport 20000 --src-vocab experiments/data/iwslt-envi/vocab.en --tgt-vocab experiments/data/iwslt-envi/vocab.vi -t experiments/data/iwslt-envi/train.en-vi.vcb.capped -d experiments/data/iwslt-envi/tst2012.en-vi.vcb.capped --model-path <your-path>/models/iwslt-envi -e 50 --lr-eta 0.1 --lr-patience 10 --patience 20 --lr-eta-decay 2 --encoder-emb-dropout-p 0.1 --encoder-sublayer-dropout-p 0.1 --decoder-emb-dropout-p 0.1 --decoder-sublayer-dropout-p 0.1 --attention-dropout-p 0.0 --ff-dropout-p 0.1 --ff-activation-type 1 --nlayers 2 --num-units 128 --num-heads 2

All model files will be saved to: experiments/models/envi.
	Preparing to train the model from scratch...

Loading vocabularies from files...
	Source vocabulary file: experiments/data/iwslt-envi/vocab.en
	Target vocabulary file: experiments/data/iwslt-envi/vocab.vi
	Source vocabluary: 17191
	Target vocabluary: 7709

Reading training data from experiments/data/iwslt-envi/train.en-vi.vcb.capped...
	133141 lines, 2963637 & 3566855 tokens (s & t), 17191 & 7709 types
	Reading dev data from experiments/data/iwslt-envi/tst2012.en-vi.vcb.capped...
	1553 lines, 31089 & 37403 tokens (s & t)

Count of model parameters: 4194333

Creating minibatches for training data (using minibatch_size=1024)...

***SHUFFLE
	[lr=0.1 clips=14 updates=14] sents=228 src_unks=125 trg_unks=60 E=8.17044 ppl=3534.88 (11185.7 words/sec)
	[lr=0.1 clips=14 updates=14] sents=415 src_unks=254 trg_unks=104 E=7.7529 ppl=2328.31 (11637.7 words/sec)
	[lr=0.1 clips=7 updates=7] sents=603 src_unks=315 trg_unks=131 E=7.58751 ppl=1973.38 (11092.6 words/sec)
	[lr=0.1 clips=10 updates=10] sents=821 src_unks=401 trg_unks=170 E=7.43301 ppl=1690.89 (11051.6 words/sec)
	[lr=0.1 clips=10 updates=10] sents=1012 src_unks=501 trg_unks=214 E=7.29535 ppl=1473.43 (11139.5 words/sec)
	[lr=0.1 clips=11 updates=11] sents=1204 src_unks=590 trg_unks=244 E=7.18671 ppl=1321.75 (11217.5 words/sec)
	[lr=0.1 clips=9 updates=9] sents=1407 src_unks=669 trg_unks=283 E=7.11415 ppl=1229.24 (11182.6 words/sec)
	[lr=0.1 clips=12 updates=12] sents=1608 src_unks=783 trg_unks=327 E=7.03215 ppl=1132.46 (11279.1 words/sec)
	[lr=0.1 clips=11 updates=11] sents=1801 src_unks=871 trg_unks=356 E=6.9647 ppl=1058.59 (11325.3 words/sec)
	[lr=0.1 clips=10 updates=10] sents=2022 src_unks=965 trg_unks=387 E=6.90368 ppl=995.933 (11242.2 words/sec)
	[lr=0.1 clips=11 updates=11] sents=2200 src_unks=1060 trg_unks=433 E=6.84401 ppl=938.241 (11326.5 words/sec)
	[lr=0.1 clips=13 updates=13] sents=2412 src_unks=1163 trg_unks=534 E=6.78059 ppl=880.59 (11378 words/sec)
	[lr=0.1 clips=8 updates=8] sents=2627 src_unks=1236 trg_unks=568 E=6.74264 ppl=847.795 (11281.7 words/sec)
	[lr=0.1 clips=7 updates=7] sents=2803 src_unks=1308 trg_unks=594 E=6.71087 ppl=821.286 (11220.3 words/sec)
	...

Note that transformer-train will automatically detect existing trained models specified in the <model-path> folder path and resume the training cycle if required.

Alternatively, we can train a larger model (similar to the model configuration in the original paper) on the training set, i.e.,

nice ./build_gpu/transformer-train --dynet-devices GPU:0 --max-seq-len 150 --minibatch-size 1024  --treport 200 --dreport 20000 --src-vocab <your-path>/data/iwslt-envi/vocab.en --tgt-vocab <your-path>/data/iwslt-envi/vocab.vi -t <your-path>/data/iwslt-envi/train.en-vi.vcb.capped -d <your-path>/data/iwslt-envi/tst2012.en-vi.vcb.capped u --model-path <your-path>/models/iwslt-envi -e 50 --lr-eta 0.1 --lr-patience 10 --patience 20 --lr-eta-decay 2 --encoder-emb-dropout-p 0.1 --encoder-sublayer-dropout-p 0.1 --decoder-emb-dropout-p 0.1 --decoder-sublayer-dropout-p 0.1 --attention-dropout-p 0.0 --ff-dropout-p 0.1 --ff-activation-type 1 --nlayers 6 --num-units 512 --num-heads 8 &><your-path>/models/iwslt-envi/log.en-vi.transformer.h8_l6_u512_do010101010001_att1_ls00_pe1_ml150_ffrelu &

If the training is successfully executed, it will create the following recipe files in the <model-path> folder path:

model.params
	model.config
	src.vocab
	tgt.vocab

For decoding/inference, we can use the integrated ensemble decoder, i.e.,

./build_gpu/transformer-decode --dynet-devices GPU:0 --model-path <your-path>/models/iwslt-envi --beam 5 -T experiments/data/iwslt-envi/tst2013.en.vcb.capped > experiments/models/iwslt-envi/translation-beam5.test2013.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu

Decoding with n-best list can be performed, e.g.,

./build_gpu/transformer-decode --dynet-devices GPU:0 --model-path <your-path>/models/iwslt-envi --beam 5 --topk 100 --nbest-style moses -T experiments/data/iwslt-envi/tst2013.en.vcb.capped > experiments/models/iwslt-envi/100besttranslations-beam5.test2013.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu

resulting in the following format:

<line_number1> ||| source ||| target1 ||| TransformerModelScore=score1 || total_score1
    <line_number2> ||| source ||| target2 ||| TransformerModelScore=score2 || total_score2
    ...
	
The decoding configuration file inside the model folder (e.g., experiments/models/iwslt-envi/model.config) has the following format:

<num-units> <num-heads> <nlayers> <ff-num-units-factor> <encoder-emb-dropout> <encoder-sub-layer-dropout> <decoder-emb-dropout> <decoder-sublayer-dropout> <attention-dropout> <ff-dropout> <use-label-smoothing> <label-smoothing-weight> <position-encoding-type> <position-encoding-flag> <max-seq-len> <attention-type> <ff-activation-type> <use-shared-embeddings> <use-hybrid-model> <your-trained-model-path>

Note that, during training, the configuration file will be automatically created in the <model-path> folder path.

The following is an example of configuration file,

128 2 2 4 0.1 0.1 0.1 0.1 0.1 0.1 0 0.1 1 0 300 1 1 0 0 <your-path>/models/iwslt-envi/params.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu
    
It's worth noting that we can have multiple models for ensemble decoding, i.e.,

128 2 2 4 0.1 0.1 0.1 0.1 0.1 0.1 0 0.1 1 0 300 1 1 0 0 <your-path>/models/iwslt-envi/params.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu_run1
    128 2 2 4 0.1 0.1 0.1 0.1 0.1 0.1 0 0.1 1 0 300 1 1 0 0 <your-path>/models/iwslt-envi/params.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu_run2

Suppose all these models use the same vocabulary sizes.

Finally, we can evaluate the translation result with BLEU:

<your-moses-path>/mosesdecoder-RELEASE-3.0/scripts/generic/multi-bleu.perl <your-path>/data/iwslt15-envi/tst2013.vi < <your-path>/models/iwslt-envi/translation-beam5.test2013.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu > <your-path>/models/iwslt-envi/translation-beam5.test2013.en-vi.transformer.h2_l2_u128_do010101010001_att1_ls00_pe1_ml150_ffrelu.score-BLEU

Note that it is recommended to use sacreBLEU or mteval instead for fairest evaluation(s).

Additionally, Transformer-DyNet also provides the training of language model based on transformer architecture, e.g.,

mkdir  <your_folder>/lm

nice ./build_gpu/transformer-lm --max-seq-len 100 --minibatch-size 1024 --treport 512 --dreport 20000 --vocab <vocab_file> -t <training_data_file> -d <dev_data_file> -p <your_folder>/lm -e 100 --lr-eta 0.1 --lr-patience 10 --patience 20 --lr-eta-decay 2 --emb-dropout-p 0.1 --sublayer-dropout-p 0.1 --attention-dropout-p 0.1 --ff-dropout-p 0.1 --ff-activation-type 1 --nlayers 4 --num-units 512 --num-heads 4 --use-label-smoothing --label-smoothing-weight 0.1 --position-encoding 2 --reset-if-stuck --use-smaller-minibatch --num-resets 5 &><your_folder>/lm/train.log &

Note that: <training_data_file> and <dev_data_file> have to be created by ./scripts/wrap-data.py as follows:

./scripts/wrap-data.py <train_file> <dev_file> <test_file> [<vocab_file>|<word_cutoff_freq>]

--vocab <vocab_file> can be used if you already have the existing vocabulary file, otherwise ignore it.

## Benchmarks on Sequence-to-Sequence Generation Tasks

The details of my benchmarks (scripts, results, scores) can be found in 'benchmarks' folder.

### Language Modeling (to be updated)

### Machine Translation

#### IWSLT English-Vietnamese

* Data for English --> Vietnamese (train: 133141; dev: 1553; test: 1268; vocab 17191 (en) & 7709 (vn) types), can be obtained from https://github.com/tensorflow/nmt.

BLEU (tokenized + case-sensitive)
								test2012(dev)		test2013(test)		PPLX(dev)
	- NMT (https://github.com/tensorflow/nmt)		23.8			26.1			-
	(1 biLSTM-layer encoder, 2 LSTM-layer decoders, 512 hidden/embedding dim, 512 attention dim, dropout 0.2 for attention, SGD, beam10)
	- (Luong & Manning, 2015)				-			23.3			-
	(https://nlp.stanford.edu/pubs/luong-manning-iwslt15.pdf)
	------------------------------------------------------------------------------------------------------------------
	Mantidae (https://github.com/duyvuleo/Mantidae)
	- Baseline (attentional model)				-			23.94			13.6704
	(1 bi-LSTM encoder, 2 LSTM decoders, 512 hidden/embedding dim, 512 attention dim, SGD, beam5)
		w/ LSTM dropout (0.2) for encoder/decoder	-			24.96			13.0963			
	------------------------------------------------------------------------------------------------------------------
	tensor2tensor						-			27.69 (multi-bleu)	-			(as of April 2018)
								-			28.47 (t2t-bleu)	-
	(data w/ wordpieces segmentation?, transformer base (8 heads, 6 layers, 512 dim), trained 500K steps, averaging 10 last checkpoints)
	------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1a (small model)
	(2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)			-			27.50			10.5622	
	- Baseline 2 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward), attentive dropout)
		and label smoothing (0.1)			-			28.85 (multi-bleu)	8.97637 		(new, latest improved version)
								-			29.23 (t2t-bleu)	
	- Ensemble models
		1 small and 1 medium models (old)		26.10			28.79			-
		2 small and 2 medium models (old)		26.91			29.53			-
		2 medium models (new)				27.11			29.72 (multi-bleu)	-			(new, latest improved version)
											30.37 (t2t-bleu)
	- Baseline 3a (w/ wordpieces segmentation)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
	- Baseline 3b (w/ BPE)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
	******************************************************************************************************************

Note/Comment: Currently, I did not use any (word) segmentation for Vietnamese (and English), just simply used all the words in the vocabularies provided from https://github.com/tensorflow/nmt. Note that tensor2tensor used wordpieces segmentation?. Also, my network is much smaller (4 heads, 4 layers). However, I still got SOTA results on the task with both single (28.85) and ensemble (29.71) models.

#### The Kyoto Free Translation Task (English-Japanese)

* Data for English --> Japanese (train (clean version): 329882; dev&dev-tune: 2401; test: 1160; vocab (src & trg freq >=3, lowercased) 51159 (en) & 51626 (ja) types), can be obtained from http://www.phontron.com/kftt/#dataonly.

BLEU (tokenized + case-insensitive)
								dev		test		PPLX(dev&dev-tune)		Comment
	- NAIST's SMT system at KFTT 2012			21.08		23.15		-
	(KyTea/GIZA++/Moses/Lader 1.0)
	- Attentional Model (Arthur et al, 2016)		-		20.86		-				test set size reported: 1169?
	(https://arxiv.org/pdf/1606.02006.pdf)
	(4 stacked LSTMs for decoders, hidden dim 800, BiLSTM encoder with input dim 1600, Adam, beam5)
		w/ translation lexicon 	integration		-		23.20		-
	--------------------------------------------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (small model)
	(2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)			-		20.77		15.0154					
	- Baseline 2 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)			-		23.53		13.2851	
	- Baseline 2* (same config. with baseline 2)
		w/ BPE (joint, 32K)				-		25.46		10.8828
		ensemble (2 different runs)			-		26.55		-		
	********************************************************************************************************************************************************

Note/Comment: For experiments done earlier, I did not use the attention dropout mechanism (by using it, the result may be much better). As seen, single transformer model (with medium network) does outperform the best SMT (with preordering in Japanese) as well as the NMT with translation lexicon integration. Also, just simply applying joint byte-pair encoding (BPE) on both English and Japanese, we can obtain much better SOTA result on the task (25.22 vs 23.53 vs. 23.20).

#### WMT17 English<->German

* Data (http://www.statmt.org/wmt17/translation-task.html) (train: 5777224 after filtering 40K_jBPE-ed sentences with length >=80; dev: newstest2013), preprocessed data can be obtained from http://data.statmt.org/wmt17/translation-task/preprocessed/.

*********************************************************************************************************************************************
	* DE-->EN (single system)
	
		                                		BLEU (tokenized + case-sensitive)
		                                        newstest2014            	newstest2015            newstest2016		newstest2017
	- Stanford NMT (Luong et al., 2015)                                     	24.9
	(top50K, UNK post replacement, 8 stacking LSTM encoder/decoder layers, 1000 hidden/embedding dim, SGD, dropout 0.2)
	------------------------------------------------------------------------------------------------------------------
	- Edinburgh NMT (Sennrich et al., 2016a)                                	26.4                    28.5
	(89500 shared BPE, ? GRU encoders, ? GRU decoders, 500 embedding dim, 1024 hidden dim, AdaDelta, beam12, pervasive dropout)
	-  (Sennrich et al., 2016b)        		29.5                    	30.4
	(back-translation with monolingual data, 89500 shared BPE, ? GRU encoders, ? GRU decoders, 620 embedding dim, 1000 hidden dim, AdaDelta, beam12)
	------------------------------------------------------------------------------------------------------------------
	- Google's NMT									29.9
	(https://github.com/tensorflow/nmt)
	(NMT + GNMT attention (beam=10), 4 LSTM encoders and decoders, 1024 units, jBPE 32K)
	------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward), attentive dropout)
		and label smoothing (0.1)		30.63 				30.91 			36.84 			31.77 
	- Advance 1 (medium model)			31.96				32.49			40.22			34.25
	(same configuration with Baseline 1)
	(use back-translated data from http://data.statmt.org/rsennrich/wmt16_backtranslations/)
	*********************************************************************************************************************************************
	* DE-->EN (single system)
	
		                                		BLEU (detokenized + case-sensitive) (w/ mteval-v13a.pl script, official evaluation)
		                                       		newstest2016			newstest2017			#params			Notes	
	- WMT competition (SOTA)				38.6				35.1
	(http://matrix.statmt.org)
	(newstest2016: BPE neural MT system with monolingual training data (back-translated). ensemble of 4, reranked with right-to-left model.)
	(newstest2017: BPE neural MT system with monolingual training data (back-translated). ensemble of 4 L2R and 4 R2L models.)
	------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (medium model, single best)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward), attentive dropout)
		and label smoothing (0.1)			35.85				30.80				62.6M			12 epochs, training took 7-10 days
	- Advance 1 (medium model, single best)			39.00 (new SOTA)		33.17				62.6M
	(same configuration with Baseline 1)
	(use back-translated data from http://data.statmt.org/rsennrich/wmt16_backtranslations/)
	*********************************************************************************************************************************************
	Note/Comment: Single Transformer systems (without using back-translated data) are still far away from the WMT SOTAs. Unsuprisingly, the single Transformer system enhanced with back-translated data outperformed the ensemble systems from WMT on newstest2016.

*********************************************************************************************************************************************
	* EN-->DE (single system)
	
		                                		BLEU (tokenized + case-sensitive) (w/ multi-bleu.perl script)
		                                        newstest2014            	newstest2015            newstest2016		newstest2017		Notes
	- Google's NMT					23.7				26.5
	(https://github.com/tensorflow/nmt)
	(NMT + GNMT attention (beam=10), 4 LSTM encoders and decoders, 1024 units, jBPE 32K)
	- WMT competition (SOTA)			20.6				24.9
	(note: these BLEU scores may be with detokenisation)
	- OpenNMT (Klein et al., 2017)			19.3				-
	- tf-seq2seq (Britz et al., 2017)		22.2				25.2
	- GNMT (Wu et al., 2016)			24.6
	- Google's tensor2tensor			27.3 (SOTA)
	(original Transformer paper, Vaswani et al, 2017)
	(8 heads, 6 encoder/decoder layers, sinusoid positional encoding, 512 units, adaptive Adam, modified beam search with width 10-12, average best model)
	------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward), attentive dropout)
		and label smoothing (0.1)		27.06				29.11			32.94 			27.11 			12 epochs, training took 7-10 days
	- Advance 1 (medium model)			28.88				30.05			34.81			28.37
	(same configuration with Baseline 1)
	(use back-translated data from http://data.statmt.org/rsennrich/wmt16_backtranslations/)
	*********************************************************************************************************************************************
	* EN-->DE (single system)
	
		                                		BLEU (detokenized + case-sensitive) (w/ mteval-v13a.pl script, official evaluation)
		                                       		newstest2016			newstest2017			#params			Notes
	- Google's tensor2tensor								26.34				60.7M
	(Vaswani et al, 2017)
	(8 heads, 6 encoder/decoder layers, sinusoid positional encoding, 512 units, adaptive Adam, modified beam search with width 10-12, average best model)	
	- Amazon's Sockeye									27.50				62.9M
	(8 heads, 6 encoder/decoder layers, sinusoid positional encoding, 512 units, adaptive Adam, modified beam search with width 10-12, average best model)	
	- WMT competition (SOTA)				34.2				28.3
	(http://matrix.statmt.org)
	(newstest2016: BPE neural MT system with monolingual training data (back-translated). ensemble of 4, reranked with right-to-left model)
	(newstest2017: BPE neural MT system with monolingual training data (back-translated). ensemble of 4 L2R and 4 R2L models)
	------------------------------------------------------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (medium model, single best)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward), attentive dropout)
		and label smoothing (0.1)			32.32				26.26 				62.6M			12 epochs, training took 7-10 days
	- Advance 1 (medium model)				34.01				27.28				62.6M
	(same configuration with Baseline 1)
	(use back-translated data from http://data.statmt.org/rsennrich/wmt16_backtranslations/)
	*********************************************************************************************************************************************
	Note/Comment: Transformer-DyNet without careful optimisation of hyperparameters and with smaller networks can produce robust models with competitive results to tensor2tensor and sockeye.

#### NIST English-Chinese (in plan)

## Abstractive Summarisation

* Data for English (train (Gigaword): 3803957; dev (Gigaword, random): 6000; 2 tests: 2000 Gigaword samples and DUC 2004 test set; vocab (src & trg freq >=5): 119506 (article) & 68886 (title)  types), can be obtained from https://github.com/harvardnlp/sent-summary. Evaluation with ROUGE 1.5.5 (75-byte length limit).

=========================================================================================================
	2000 sampled sentences from Annotated Gigaword dataset provided by Prof. Rush
		                			ROUGE-1(F1)     ROUGE-2(F1)     ROUGE-L(F1)
	- ABS+                    			29.55           11.32           26.42
	(Rush et al., 2015)
	- Pointer Net             			35.19	        16.66           32.51
	(Gulcehre et al., 2016)
	- Noisy Channel
	(Yu et al., 2017)       			34.41           16.86           31.83
	- OpenNMT                 			33.13           16.09           31.00
	(http://opennmt.net/Models/)
	-------------------------------------------------------------------
	Mantidae (https://github.com/duyvuleo/Mantidae)
	- Baseline (attentional model)			34.599          16.385          32.538 
	(deep NMT, 2 source LSTM layers, 2 target LSTM layers with dropout 0.1, 512 embedding dim, 512 hidden dim, SGD, beam5)
		w/ 100-best reranking			35.819          17.127          33.349
		(bidirectional)
	-------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (small model)
	(2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5)
		w/ dropout (0.1)			34.466		16.239		32.203
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)		
			w/ BPE (40K)			34.743		16.825		32.557
	- Baseline 2 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)			36.130		17.482		33.846
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)		
			w/ BPE (40K)			35.739		17.552		33.360
	=========================================================================================================
	DUC 2004 (licensed data from LDC which needs a purchase)
	                        			ROUGE-1         ROUGE-2         ROUGE-L
	ABS+                    			28.18           8.49		23.81
	(Rush et al., 2015)
	RAS-Elman (beam10)      			28.97		8.26            24.06
	(Chopra et al., 2016) 
	-------------------------------------------------------------------
	Mantidae (https://github.com/duyvuleo/Mantidae)
	- Baseline (attentional model)			26.152          8.408           23.665
	(deep NMT, 2 source LSTM layers, 2 target LSTM layers with dropout 0.1, 512 embedding dim, 512 hidden dim, SGD, beam5)
		w/ relaxed opt				26.354          8.622           23.920
		(bidirectional) (Hoang et al., 2017)
	-------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (small model)
	(2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)		-		-		-
			w/ BPE (40K)			27.269		9.308		24.584
	- Baseline 2 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)			27.793		9.168		25.070
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)		
			w/ BPE (40K)			28.061		9.618		25.305

## Word Ordering

* Data for experiments following https://github.com/allenschmaltz/word_ordering/blob/master/data/preprocessing/README_DATASET_CREATION.txt. Standard split from PTB dataset: train (39832), valid (1700), test (2416)

Method									BLEU
	
	-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

vanilla LSTM language model (Schmaltz et al, 2016)			26.8

RNN-based seq2seq models (Wiseman et al., 2016)				31.0
		- w/ beam search optimisation (BSO)				34.5

-------------------------------------------------------------------
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 								38.48 (new SOTA)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)

## Sequence-to-Sequence based Dependency Parsing (English) (updating)

* Experiments with Penn Tree Bank WSJ corpus (train: sec2-21; dev: sec22; test: sec23)

Method							UAS		LAS		#<SHIFTs	#>SHIFTs	#ROOT_ERRORs		Note

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
	(Methods based on transition-based neural networks)

(Chen and Manning, 2014)				91.8		89.6		-		-		-

StackedLSTM (Dyer et al., 2016)				93.1		90.9		-		-		-

GloNorm (Andor et al., 2016)
		- beam 1					93.17		91.18		-		-		-
		- beam 32					94.61		92.79		-		-		-			SOTA (best reported results)
	-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
	(Methods based on neural sequence to sequence learning)

seq2seq (Wiseman et al., 2016)
		- beam 5					88.53		84.16		-		-		-
	seq2seq + BSO (Wiseman et al., 2016)
		- beam 5					91.00		87.18		-		-		-
	seq2seq + ConBSO (Wiseman et al., 2016)
		- beam 5					91.25		86.92		-		-		-

*********
	Mantidae (https://github.com/duyvuleo/Mantidae)
	(Configuration: 1 LSTM encoder layer, 2 LSTM decoder layers, bidirectional, 512 input/hidden dims, 256 attention dim, source word frequency cutoff 2, case-sensitive in source, actions: SHIFT + LEFT_ARCs + RIGHT_ARCs with 79 types, w/ incremental training using decoder dropout 0.1 (Gal et al., 2016), SGD)
		- beam 5					91.02		88.55		7		5		3

*********
	Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet)
	- Baseline 1 (small model)
	(2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)							
	- Baseline 2 (medium model)
	(4 heads, 4 encoder/decoder layers, sinusoid positional encoding, 512 units, SGD, beam5)
		w/ dropout (0.1)					
		(source and target embeddings, sub-layers (attention + feedforward))
		and label smoothing (0.1)

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

## Limitation

Currently, this implementation supports single GPU only since DyNet has not fully supported multi-GPU yet. Transformer-DyNet may be a bit slower than available toolkits (e.g., tensor2tensor, marian, sockeye, OpenNMT-tf); however, it can produce very consistent translation performance without much hyperparameter tunning even with medium networks (e.g., 4 heads and 4 encoder/decoder layers, check my results above). Supporting multi-GPUs will be my major work in the future.

## ToDo

1. implementation for Bahdanau attention type? (seem to be infeasible with current implementation)

2. speed up the decoding process of Transformer by caching technique (like in tensor2tensor) or other? Also batch decoding?

3. weighted transformer (https://arxiv.org/pdf/1711.02132.pdf, ICLR'18 rejected)

4. average model around last N best checkpoints (DyNet model parameters may be not trivial to do this!)

5. adaptive learning rate following original transformer's paper (need this?)

6. hybrid architecture between Transformer and RNN-based Enc-Dec, RNMT+ as in https://arxiv.org/pdf/1804.09849.pdf (ACL2018)?

6. other new ideas?

## Contacts

Hoang Cong Duy Vu (vhoang2@student.unimelb.edu.au; duyvuleo@gmail.com)

---
Updated Mar 2018

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