Usage of AMN SRL

This is the code for paper semantic role labeling with associated memory network.

Brief description

We've proposed to use associated memory network to better solve the SRL task. The picture below gives a brief architecture.

Step.0 Environment

This program needs the following packages to run:

Python == 3.6.5
anaconda == 3-5.2.0
PyTorch == 0.4.1
Allennlp == 0.7.1
NumPy == 1.15.4

And need to run on Linux.

Please download the ELMo pretrained config and embedding from their website and put them under models folder.

Their name should be elmo_2x4096_512_2048cnn_2xhighway_options.json and elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5.

If you want to use other versions of configs, you need to change some parameters in final_model.py.

Step.1 Preprocessing

Put the train dev test data under ./data and name them as train.txt, dev.txt, test.txt.

Also put the pretrained 100d glove embedding under ./data, and name it as glove.100d.txt.

Then run:

python preprocess.py

For preprocessing.

Step.2 Calculate distance

Put the calculated distance file under ./temp.

Name the distance between the preprocressed train and preprocressed train, preprocressed dev, preprocressed test as train_train.bin, train_dev.bin, train_test.bin.

These files should be saved using pickle under write-byte mode.

The object distance loaded from these files should be as type: List[List[Int]]. Where distance[i][j] stands for the idx of j-th nearest sentence in train set w.r.t the i-th sentence in train/dev/test set.

Here we provide the code for edit distance method, which performs best among all the distance methods.

run

python edit_dis.py train
python edit_dis.py dev
python edit_dis.py test

to get train_train.bin, train_dev.bin and train_test.bin

Step.3 Train the model

First, please specify the hyper-parameters in main.py (or leave them alone for the best performance).

Then run

python main.py

to train the model.

After some time, you will get something like below:

Step.4 Cite our paper

If you want to cite our paper, you can cite as following:

@inproceedings{guan2019AMN_SRL,
    title = {Semantic Role Labeling with Associated Memory Network},
    author = {Guan, Chaoyu and Cheng, Yuhao and Zhao, Hai},
    booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)},
    volume = {1},
    pages = "{3361--3371},
    year = {2019}
}