@inproceedings{shurtz-etal-2024-effects,
    title = "The Effects of Pretraining in Video-Guided Machine Translation",
    author = "Shurtz, Ammon  and
      Sorenson, Lawry  and
      Richardson, Stephen D.",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1380",
    pages = "15888--15898",
    abstract = "We propose an approach that improves the performance of VMT (Video-guided Machine Translation) models, which integrate text and video modalities. We experiment with the MAD (Movie Audio Descriptions) dataset, a new dataset which contains transcribed audio descriptions of movies. We find that the MAD dataset is more lexically rich than the VATEX dataset (the current VMT baseline), and we experiment with MAD pretraining to improve performance on the VATEX dataset. We experiment with two different video encoder architectures: a Conformer (Convolution-augmented Transformer) and a Transformer. Additionally, we conduct experiments by masking the source sentences to assess the degree to which the performance of both architectures improves due to pretraining on additional video data. Finally, we conduct an analysis of the transfer learning potential of a video dataset and compare it to pretraining on a text-only dataset. Our findings demonstrate that pretraining with a lexically rich dataset leads to significant improvements in model performance when models use both text and video modalities.",
}

We propose an approach that improves the performance of VMT (Video-guided Machine Translation) models, which integrate text and video modalities. We experiment with the MAD (Movie Audio Descriptions) dataset, a new dataset which contains transcribed audio descriptions of movies. We find that the MAD dataset is more lexically rich than the VATEX dataset (the current VMT baseline), and we experiment with MAD pretraining to improve performance on the VATEX dataset. We experiment with two different video encoder architectures: a Conformer (Convolution-augmented Transformer) and a Transformer. Additionally, we conduct experiments by masking the source sentences to assess the degree to which the performance of both architectures improves due to pretraining on additional video data. Finally, we conduct an analysis of the transfer learning potential of a video dataset and compare it to pretraining on a text-only dataset. Our findings demonstrate that pretraining with a lexically rich dataset leads to significant improvements in model performance when models use both text and video modalities.
Shurtz, Ammon Sorenson, Lawry Richardson, Stephen D.
The Effects of Pretraining in Video-Guided Machine Translation