1) bash train_ende_base_stage1.sh # Suppose the generated checkpoint file is located in path1

2) bash train_ende_base_stage2.sh # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
3) python thumt_stage1_code/thumt/scripts/combine_add.py --model path2 --part path1 --output path3  # copy the weight of the first stage to the second stage.
4) bash train_ende_base_stage2.sh # Here, set the --output=path3 and the training_step=first_stage_step + 5,000; Suppose the generated checkpoint file is path4

5) bash test_ende_stage2.sh # set the checkpoint file path to path4 in this script. # Suppose the predicted file is located in path5 at checkpoint step xxxxx

6) python SacreBLEU_TER_Coherence_Evaluation_code/cal_bleu_ter4ende.py # Please correctly set the golden file and predicted file in this file and in sacrebleu_ende.py, respectively.

7) python SacreBLEU_TER_Coherence_Evaluation_code/train_word2vec.py # firstly downloading the corpus in [2] and then training the word2vec.
8) python SacreBLEU_TER_Coherence_Evaluation_code/eval_coherence.py # putting the file containing three precoding utterances and the predicted file in corresponding location and then running it.

@inproceedings{liang-etal-2021-towards,
    title = "Towards Making the Most of Dialogue Characteristics for Neural Chat Translation",
    author = "Liang, Yunlong  and
      Zhou, Chulun  and
      Meng, Fandong  and
      Xu, Jinan  and
      Chen, Yufeng  and
      Su, Jinsong  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.6",
    pages = "67--79",
}