QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
CoRR(2024)
摘要
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC)
often faces the challenge of dealing with semantically identical but
format-variant inputs. Our work introduces a novel approach, called the “Query
Latent Semantic Calibrator (QLSC)”, designed as an auxiliary module for
existing MRC models. We propose a unique scaling strategy to capture latent
semantic center features of queries. These features are then seamlessly
integrated into traditional query and passage embeddings using an attention
mechanism. By deepening the comprehension of the semantic queries-passage
relationship, our approach diminishes sensitivity to variations in text format
and boosts the model's capability in pinpointing accurate answers. Experimental
results on robust Question-Answer datasets confirm that our approach
effectively handles format-variant but semantically identical queries,
highlighting the effectiveness and adaptability of our proposed method.
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