liuyueling2020/selective-attn

Data and code used in our NAACL'19 paper "Selective Attention for Context-aware Neural Machine Translation"

This is an implementation of our paper:

Sameen Maruf, Andre F. T. Martins and Gholamreza Haffari: Selective Attention for Context-aware Neural Machine Translation. Accepted at NAACL-HLT 2019.

Please cite our paper if you use our code.

Dependencies

Before compiling dynet, you need:

• Eigen, using the development version (not release), e.g. 3.3.beta2 (http://bitbucket.org/eigen/eigen/get/3.3-beta2.tar.bz2)

• cuda version 7.5 or higher

• boost, e.g., 1.58 using libboost-all-dev ubuntu package

• cmake, e.g., 3.5.1 using cmake ubuntu package

Building

First, clone the repository

git clone https://github.com/sameenmaruf/selective-attn.git

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

hg clone https://bitbucket.org/eigen/eigen/ -r 346ecdb

A version of latest DyNet (v2.0.3) is already included (e.g., dynet folder).

CPU build

Compiling to execute on a CPU is as follows

mkdir build_cpu
cd build_cpu
cmake .. -DEIGEN3_INCLUDE_DIR=EIGEN_PATH [-DBoost_NO_BOOST_CMAKE=ON]
make -j 2

Boost note. The "-DBoost_NO_BOOST_CMAKE=ON" can be optional but if you have a trouble of boost-related build error(s), adding it will help to overcome.

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 -DBoost_NO_BOOST_CMAKE=TRUE -DENABLE_BOOST=TRUE

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

This will build the following binaries

build_cpu/transformer-train
build_cpu/transformer-decode
build_cpu/transformer-context
build_cpu/transformer-context-decode
build_cpu/transformer-computerep

GPU build

Building on the GPU uses the Nvidia CUDA library, currently tested against version 8.0.61. The process is as follows

mkdir build_gpu
cd build_gpu
cmake .. -DBACKEND=cuda -DCUDA_TOOLKIT_ROOT_DIR=CUDA_PATH -DEIGEN3_INCLUDE_DIR=EIGEN_PATH -DMKL=TRUE -DMKL_ROOT=MKL_PATH -DBoost_NO_BOOST_CMAKE=TRUE -DBoost_NO_SYSTEM_PATHS=TRUE -DENABLE_BOOST=TRUE
make -j 2

substituting in your EIGEN_PATH and CUDA_PATH folders, as appropriate. Make sure to include MKL if using sparsemax as it is implemented for CPU.

This will result in the binaries

build_gpu/transformer-train
build_gpu/transformer-decode
build_gpu/transformer-context
build_gpu/transformer-context-decode
build_gpu/transformer-computerep

Using the model

See readme_commands.txt

References

The original Transformer implementation used in our code is available at https://github.com/duyvuleo/Transformer-DyNet

Contacts

Sameen Maruf ([email protected]; [email protected])

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Updated June 2019