Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models
Retriever-reader models achieve competitive performance across many different NLP tasks such as open question answering and dialogue conversations. In this work, we notice these models easily overfit the top-rank retrieval passages and standard training fails to reason over the entire retrieval passages. We introduce a learnable passage mask mechanism which …