  The rVRAIN team tackled the Budget Argument Mining (BAM) task, consisting of
a combination of classification and information retrieval sub-tasks. For the
argument classification (AC), the team achieved its best performing results
with a five-class BERT-based cascade model complemented with some handcrafted
rules. The rules were used to determine if the expression was monetary or not.
Then, each monetary expression was classified as a premise or as a conclusion
in the first level of the cascade model. Finally, each premise was classified
into the three premise classes, and each conclusion into the two conclusion
classes. For the information retrieval (i.e., relation ID detection or RID),
our best results were achieved by a combination of a BERT-based binary
classifier, and the cosine similarity of pairs consisting of the monetary
expression and budget dense embeddings.
