Before revision:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and solve a subgraph alignment problem to find a part of the context which matches the question. Our model uses BERT to compute alignment scores , and by using structured SVM, we can train end-to-end despite complex inference.
After revision:  We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and solve a subgraph alignment problem to find a part of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite complex inference.