AMR-parser

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AMR-parser. Code for EMNLP2019 paper "Core Semantic First: A Top-down Approach for AMR Parsing."

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# AMR-parser
Code for our **EMNLP2019** paper,

**Core Semantic First: A Top-down Approach for AMR Parsing**. [[paper]](https://www.aclweb.org/anthology/D19-1393.pdf)[[bib]](https://www.aclweb.org/anthology/D19-1393.bib)

Deng Cai and Wai Lam.

# Requirement

python2==Python2.7

python3==Python3.6

`sh setup.sh` (install dependencies)

# Preprocessing

in the directory `preprocessing`
1. make a directory, for example `preprocessing/2017`. Put files `train.txt`, `dev.txt`,`test.txt`in it. The format of these files be the same as our example file `preprocessing/data/dev.txt`.
2. `sh go.sh` (you may make necessary changes in `convertingAMR.java`)
3. `python2 preprocess.py`
- We use Stanford Corenlp to extract NER, POS and lemma, see `go.sh` and `convertingAMR.java` for details.
- We already provided the alignment information used for concept prediction as in `common/out_standford` (LDC2017T10 by the aligner of [Oneplus/tamr](https://github.com/Oneplus/tamr)).

# Training

in the directory  `parser`
1. `python3 extract.py && mv *vocab *table ../preprocessing/2017/.` Make vocabularies for the dataset in `../preprocessing/2017` (you may make necessary changes in `extract.py` and the command line as well)
2. `sh train.sh` Be patient! Checkpoints will be saved in the directory `ckpt` by default. (you may make necessary changes in `train.sh`).

# Testing

in the directory  `parser`
1. `sh work.sh` (you should make necessary changes in `work.sh`)
- The most important argument is `--load_path`, which is supposed to be set to a specific checkpoint file, for example, `somewhere/some_ckpt`. The output file will be in the same folder with the checkpoint file, for example, `somewhere/some_ckpt_test_out`

# Evaluation

in the directory `amr-evaluation-tool-enhanced`
1. `python2 smatch/smatch.py --help`
  A large of portion of the code under this directory is borrowed from [ChunchuanLv/amr-evaluation-tool-enhanced](https://github.com/ChunchuanLv/amr-evaluation-tool-enhanced), we add more options as follows.

```shell
    --weighted           whether to use a weighted smatch or not
    --levels LEVELS      how deep you want to evaluate, -1 indicates unlimited, i.e., full graph
    --max_size MAX_SIZE  only consider AMR graphs with limited size <= max_size, -1 indicates no limit
    --min_size MIN_SIZE  only consider AMR graphs with limited size >= min_size, -1 indicates no limit
  ```

For examples:

2. To calculate the smatch-weighted metric in our paper.

`python2 smatch/smatch.py --pr -f parsed_data golden_data --weighted`

3. To calculate the smatch-core metric in our paper

`python2 smatch/smatch.py --pr -f parsed_data golden_data --levels 4`

# Pretrained Model

We release our pretrained model at [Google Drive](https://drive.google.com/open?id=1UOB_Czv0plHhindZRR_dwdnPZhBwqNES).

To use the pretrained model, move the vocabulary files under `[Google Drive]/vocabs` to `preprocessing/2017/` and adjust `work.sh` accordingly (set `--load_path` point to `[Google Drive]/model.ckpt`).

We also provide the *exact* model output reported in our paper. The output file and the corresponding reference file are in the `legacy` folder.

# Citation

If you find the code useful, please cite our paper.
```
@inproceedings{cai-lam-2019-core,
    title = "Core Semantic First: A Top-down Approach for {AMR} Parsing",
    author = "Cai, Deng  and
      Lam, Wai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1393",
    pages = "3790--3800",
}
```
# Contact
For any questions, please drop an email to [Deng Cai](https://jcyk.github.io/).

AMR-parser

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