Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation
This repo hosts the code to accompany the camera-ready version of “Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation” in EMNLP 2021.
Setup
We provide our scripts and modifications to Fairseq. In this section, we describe how to go about running the code and, for instance, reproduce Table 2 in the paper.
Data
To view the data as we prepared and used it, switch to the main branch. But we recommend cloning code from this branch to avoid downloading a large amount of data at once. You can always obtain any data as necessary from the main branch.
Installations
We worked in a conda environment with Python 3.8.