Data and code for the ACL2022 main conference paper MSCTD: A Multimodal Sentiment Chat Translation Dataset.
Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in 3,079 bilingual dialogues. Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label. Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT. Preliminary experiments on four language directions (English-Chinese and English-German) verify the potential of contextual and multimodal information fusion and the positive impact of sentiment on the MCT task. Additionally, as a by-product of the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment analysis. We hope it can also facilitate research on both multimodal chat translation and multimodal dialogue sentiment analysis.
An example is shown as follows:
Our code is basically based on the publicly available toolkit: THUMT-Tensorflow (our python version 3.6, tensorflow version=1.12). The following steps are training our model and then test its performance in terms of BLEU, METEOR, and TER.
- Training M1 (Trans.) and test
1) bash train_zh2en_share_base.sh # Suppose the generated checkpoint file is located in M1_path1
2) bash test_zhen_m1.sh checkpoint_name checkpoint_step test
- Training M2 (TCT) and test
1) bash pretrain_train_tct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M1_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_train_tct.sh start_steps training_step # Here, set the --output=path3 and set the start_steps = first_stage_step and the training_step=first_stage_step + 10,000
4) bash test_zhen_tct.sh False False False checkpoint_name checkpoint_step test
- Training M3 (CA-TCT) and test
1) bash pretrain_train_ca_tct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M1_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_train_ca_tct.sh start_steps training_step # Here, set the --output=path3 and set the start_steps = first_stage_step and set the training_step=first_stage_step + 10,000
4) bash test_zhen_tct.sh True True True checkpoint_name checkpoint_step test
- Training M5 (Trans.+Sum) and test
1) bash train_multimodal_coarse_sum.sh
2) bash test_zhen_coarse_sum.sh checkpoint_name checkpoint_step test # note that when testing you should set the "trainable=True" in Line206 of src_code/c-thumt-sum/thumt/models/transformer.py, to load the image feature.
- Training M6 (Trans.+Att) and test
1) bash train_multimodal_coarse_att.sh # Suppose the generated checkpoint file is located in M6_path1
2) bash test_zhen_coarse_att.sh checkpoint_name checkpoint_step test # note that when testing you should set the "trainable=True" in Line206 of src_code/c-thumt-sum/thumt/models/transformer.py, to load the image feature.
- Training M7 (MCT) and test
1) bash pretrain_coarse_mct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M6_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_coarse_mct.sh # Here, set the --output=path3 and the training_step=first_stage_step + 5,000; Suppose the generated checkpoint file is path4
4) bash test_zhen_coarse_mct.sh False False False checkpoint_name checkpoint_step test # note that when testing you should set the "trainable=True" in Line407 of src_code/thumt-dialog-wo-sp-decoder-w-mask-all-mlp-four-coarse-nct-att2/thumt/models/transformer.py, to load the image feature.
- Training M8 (CA-MCT) and test
1) bash pretrain_coarse_ca_mct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M6_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_coarse_ca_mct.sh # Here, set the --output=path3 and the training_step=first_stage_step + 5,000; Suppose the generated checkpoint file is path4
4) bash test_zhen_coarse_mct.sh True True True checkpoint_name checkpoint_step test # note that when testing you should set the "trainable=True" in Line407 of src_code/thumt-dialog-wo-sp-decoder-w-mask-all-mlp-four-coarse-nct-att2/thumt/models/transformer.py, to load the image feature.
- Training M9 (Trans.+Con) and test
1) bash train_multimodal_fine_con_new.sh
2) bash test_zhen_fine_con.sh.sh checkpoint_name checkpoint_step test
- Training M11 (M-Trans.) and test
1) bash train_multimodal_fine_m.sh # Suppose the generated checkpoint file is located in M11_path1
2) bash test_zhen_fine_m.sh checkpoint_name checkpoint_step test
- Training M12 (MCT) and test, checkpoint, and output
1) bash pretrain_train_mct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M11_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_train_mct.sh # Here, set the --output=path3 and the training_step=first_stage_step + 5,000; Suppose the generated checkpoint file is path4
4) bash test_zhen_fine_mct.sh False False False checkpoint_name checkpoint_step test
- Training M13 (CA-MCT) and test, checkpoint, and output
1) bash pretrain_train_ca_mct.sh 1 1 # Here, set the training_step=1; Suppose the generated checkpoint file is located in path2
2) python thumt1_code/thumt/scripts/combine_add.py --model path2 --part M11_path1 --output path3 # copy the weight of the first stage to the second stage.
3) bash pretrain_train_ca_mct.sh # Here, set the --output=path3 and the training_step=first_stage_step + 5,000; Suppose the generated checkpoint file is path4
4) bash test_zhen_fine_mct.sh True True True checkpoint_name checkpoint_step test
- Test by SacreBLEU and TER (Required TER: v0.7.25; Sacre-BLEU: version.1.4.13 (BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.4.13))
1) 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.
- Test by meteor for English or German(we used meteor-1.5.)
2) java -Xmx2G -jar meteor-*.jar generated_file reference_file -norm -writeAlignments -f system1
- Test by meteor for Chinese (we used meteor-1.5.).
3) python SacreBLEU_TER_Coherence_Evaluation_code/cal_meteor_score_enzh.py path_of_generated_file start_step end_step # Note that we first transform character-level Chinese to special English word (e.g., word1, word2, ... refer to SacreBLEU_TER_Coherence_Evaluation_code/transform_to_enchar.py) and then we utilize the above command (2) to calculate meteor.
If you find this project helps, please cite our paper :)
@inproceedings{liang-etal-2022-msctd,
title = "{MSCTD}: A Multimodal Sentiment Chat Translation Dataset",
author = "Liang, Yunlong and
Meng, Fandong and
Xu, Jinan and
Chen, Yufeng and
Zhou, Jie",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.186",
doi = "10.18653/v1/2022.acl-long.186",
pages = "2601--2613",
}
Please feel free to open an issue or email me ([email protected]) for questions and suggestions.
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