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.

我们提出了一种称为 “Query Latent Semantic Calibrator (QLSC)” 的新方法，作为现有 MRC 模型的辅助模块，通过一种独特的缩放策略捕捉查询的潜在语义中心特征，并使用注意机制将这些特征与传统的查询和段落嵌入无缝集成，从而加深了对语义查询 - 段落关系的理解，降低了对文本格式变化的敏感性，提高了模型在准确定位答案方面的能力。实验结果验证了我们的方法在处理格式变异但语义相同的查询方面的有效性和适应性。

QLSC：基于查询潜在语义校准器的鲁棒抽取式问答

QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question  Answering

While most successful approaches for machine reading comprehension rely on
single training objective, it is assumed that the encoder layer can learn great
representation through the loss function we define in the predict layer, which
is cross entropy in most of time, in the case that we first use neural networks
to encode the question and paragraph, then directly fuse the encoding result of
them. However, due to the distantly loss backpropagating in reading
comprehension, the encoder layer cannot learn effectively and be directly
supervised. Thus, the encoder layer can not learn the representation well at
any time. Base on this, we propose to inject multi granularity information to
the encoding layer. Experiments demonstrate the effect of adding multi
granularity information to the encoding layer can boost the performance of
machine reading comprehension system. Finally, empirical study shows that our
approach can be applied to many existing MRC models.

研究提出在编码层注入多粒度信息来提高机器阅读理解系统的性能，并在实验中得出结果证明该方法适用于许多现有的 MRC 模型。