The system runs on docker. You need to execute the following commands.
$ docker pull masashiy/ccg2lambda $ docker run -w /root/ -d -it -v <WORKDIR>:/home --name ccg2lambda masashiy/ccg2lambda:latest $ docker exec -it ccg2lambda /bin/bash $ apt update $ apt install unzip
Then, you should clone this repository and place it on the container.
$ git clone https://github.com/ynklab/ccgtemp.git
$ cd ccgtempIt is necessary to install depccg (version 1.1.0), janome (version 0.3.9), more_itertools.
$ pip install depccg==1.1.0 janome==0.3.9 more_itertools $ depccg_ja download
In addition, vampire Ver.4.4 with Z3 and tregex Ver.3.9.2 are required. (You should rename the vampire file to "vampire".)
$ curl --create-dirs -o "vampire/vampire" https://vprover.github.io/bin/vampire_z3_rel_static_release_v4.4
$ chmod 755 vampire/vampire
$ wget https://nlp.stanford.edu/software/stanford-tregex-2018年10月16日.zip
$ unzip stanford-tregex-2018年10月16日.zip
$ rm stanford-tregex-2018年10月16日.zipYou need to write the path to the vampire and tregex directories in vampire_location.txt and tregex_location.txt respectively.
$ echo "./vampire" > vampire_location.txt $ echo "./stanford-tregex-2018年10月16日" > tregex_location.txt
Dataset is in data directory.
- data/jsem/jsem_temporal_order.csv: JSeM problems involving temporal order.
- data/plmute/cs1_test_ja.csv: test data of PLMUTE
- data/plmute/cs1_train_ja.csv: all training data of PLMUTE
- data/plmute/cs1_test_ja.csv: part of training data of PLMUTE
If you want to perform inference with a sample problem, you can run commands like the following.
$ ./rte_tsurgeon_vamp.sh sample.txt templates/semantic_templates_ja_tense.yaml ja vampire 1 templates/transform.tsgn sample_dir $ cat sample_dir/ja_results/sample.txt.answer
If you want to perform inference with a different problem, you need to delete the output of the previous inference.
If you want to perform inference with jsem, you can run following commands.
$ python3 make_problems.py jsem $ ./temporal_inference_wrap.sh jsem
The result will be output to jsem/overall_results.tsv.
If you want to perform inference with PLMUTE_ja, you can run following commands.
$ python3 make_problems.py plmute $ ./temporal_inference_wrap.sh plmute
It may take 20 hours to complete. (Depends on the performance of the execution environment)
The result will be output to plmute/overall_results.tsv.
If you use this dataset in any published research, please cite the following:
- Tomoki Sugimoto and Hitomi Yanaka. 2022. Compositional Semantics and Inference System for Temporal Order based on Japanese CCG. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 104–114, Dublin, Ireland. Association for Computational Linguistics.
@inproceedings{sugimoto-yanaka-2022-compositional, title = "Compositional Semantics and Inference System for Temporal Order based on {J}apanese {CCG}", author = "Sugimoto, Tomoki and Yanaka, Hitomi", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-srw.10", pages = "104--114", }
For questions and usage issues, please contact sugimoto.tomoki@is.s.u-tokyo.ac.jp .