Dense retrieval uses a contrastive learning framework to learn dense representations of queries and contexts.
Claims in FAVIQ are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis.
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.
Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates.