Codes and data for the ACL 2020 paper: Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

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See the project page for more details.

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Environment

• python3.7
• pytorch1.0.1
• allennlp
• huggingface-bert
• tqdm

Dataset

• Download the UPBV1.0 data. Please pre-process the data, making sure that one sentence in the data only at maximum contains one set of prd-args proposition.

• Use the UP_English data created by us.

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A. Translation-based projection

• Step1. Train POS taggers for each languages based on the corresponding labelled POS data (with universal POS tagset).

  • trainPOS.py (config the Config/config.cfg in advance)

• Step2. Prepare the translated SRL parallel data.

  • The translation process should be fulfilled by you own. You can achieve it via Google translation API.
  • The format of source side data and translated target side data should follow the example data in Data/upb_parallel/en-de fold: en-de-train-src.conllu and en-de-train-tgt.conllu. It's conllu style.
  • We provide the UPB-English data which was not offered by UPBV1.0 at the time (i.e., 2019/10/1) we perform our experiments.

• Step3. Generating the aligning file for each pair of source and target language dataset.

  • Install the fast_align, and conduct alignment.
  • The format of the alignment output file follows the example file en-de-train-src2tgt-align.prob in Data/upb_parallel/en-de fold.

• Step4. Start annotation projection.

  • project.py

Note:

• Please note that one sentence may contain multiple sets of prd-args structure. So you should pre-process the data and split them in advance, making sure that one sentence in the data only at maximum contains one set of prd-args proposition.

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B. PGN-LSTM SRL model

• step 1. Configure the setting file Config/config.cfg

• step 2. Run the file:

python main.py

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Citation

If you use our dataset, please cite the following paper:

@inproceedings{fei-etal-2020-cross,
    title = "Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus",
    author = "Fei, Hao and Zhang, Meishan and Ji, Donghong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.acl-main.627",
    pages = "7014--7026",
}