MTCue: Model Checkpoints

The model checkpoints contained here are associated with an ACL 2023 paper entitled "MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation" (citation is to be added when the Proceedings are published).

Each .zip file here contains a checkpoint to a baseline translation model and MTCue for the language pair in the name (e.g. en.de is the English-to-German language pair). How to use them is described in detail in the associated GitHub repository.

The models (checkpoints.zip) were trained in PyTorch and via the Fairseq toolkit:

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,  Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf,  A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,  Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An imperative  style, high-performance deep learning library. Advances in Neural  Information Processing Systems, 32(NeurIPS).

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings  of the 2019 Conference of the North American Chapter of the Association  for Computational Linguistics (Demonstrations), pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.

Full documentation to how to use the resources is included in the [GitHub repository](https://github.com/st-vincent1/MTCue) which contains a link to this ORDA page.