UCCA Parser
An implementation of "HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing".
This version of the implementation uses lexical features in the corpus, including POS tags, dependency labels and entity labels, just as described in the paper.
For other models or versions, please see different branches.
Requirements
python >= 3.6.0
pytorch == 1.0.0
ucca == 1.0.127Note that the code has not been tested on the newest version of ucca module.
Datasets
The datasets are all provided by SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA. The official website is https://competitions.codalab.org/competitions/19160.
Pre-trained embeddings: http://fasttext.cc
Performance
Here are the results I re-ran on June 13, 2019, which are almost the same as the results in the paper.
| description | dev primary | dev remote | dev average | test wiki primary | test wiki remote | test wiki average | test 20K primary | test 20K remote | test 20K average |
|---|---|---|---|---|---|---|---|---|---|
| English-Topdown-Lexical | 79.7 | 52.2 | 79.2 | 77.9 | 48.0 | 77.4 | 74.0 | 23.4 | 73.0 |
| German-Topdown-Lexical | 82.9 | 57.1 | 82.4 | / | / | / | 83.5 | 61.1 | 83.0 |
Usage
You can start the training, evaluation and prediction process by using subcommands registered in parser.cmds or just use the shell scripts included in.
$ python run.py -h
usage: run.py [-h] {train,predict,evaluate} ...
UCCA Parser.
optional arguments:
-h, --help show this help message and exit
Commands:
{train,predict,evaluate}
train Train a model.
predict Use a trained model to make predictions.
evaluate Evaluate the specified model and dataset.Optional arguments of the subparsers are as follows:
Note that the path to save the model is a directory. After training, there are three files in the directory which are named "config.json", "vocab.pt" and "parser.pt".
$ python run.py train -h
usage: run.py train [-h] --train_path TRAIN_PATH --dev_path DEV_PATH
[--emb_path EMB_PATH] --save_path SAVE_PATH --config_path
CONFIG_PATH [--test_wiki_path TEST_WIKI_PATH]
[--test_20k_path TEST_20K_PATH] [--gpu GPU] [--seed SEED]
[--threads THREADS]
optional arguments:
-h, --help show this help message and exit
--train_path TRAIN_PATH
train data dir
--dev_path DEV_PATH dev data dir
--emb_path EMB_PATH pretrained embedding path
--save_path SAVE_PATH
dic to save all file
--config_path CONFIG_PATH
dic to save all file
--test_wiki_path TEST_WIKI_PATH
wiki test data dir
--test_20k_path TEST_20K_PATH
20k data dir
--gpu GPU gpu id
--seed SEED random seed
--threads THREADS thread num
$ python run.py evaluate -h
usage: run.py evaluate [-h] --gold_path GOLD_PATH --save_path SAVE_PATH
[--batch_size BATCH_SIZE] [--gpu GPU] [--seed SEED]
[--threads THREADS]
optional arguments:
-h, --help show this help message and exit
--gold_path GOLD_PATH
gold test data dir
--save_path SAVE_PATH
path to save the model
--batch_size BATCH_SIZE
batch size
--gpu GPU gpu id
--seed SEED random seed
--threads THREADS thread num
$ python run.py predict -h
usage: run.py predict [-h] --test_path TEST_PATH --save_path SAVE_PATH
--pred_path PRED_PATH [--batch_size BATCH_SIZE]
[--gpu GPU] [--seed SEED] [--threads THREADS]
optional arguments:
-h, --help show this help message and exit
--test_path TEST_PATH
test data dir
--save_path SAVE_PATH
path to save the model
--pred_path PRED_PATH
save predict passages
--batch_size BATCH_SIZE
batch size
--gpu GPU gpu id
--seed SEED random seed
--threads THREADS thread num
Conversion
Conversion code is included in parser.convert. The function UCCA2tree is used to convert a UCCA passage to a tree. The function to_UCCA is used to convert a tree to a UCCA passage. Remote edge recovery code is included in parser.submodel.remote_parser.py independently.