论文标题
多语言机器阅读理解中的零摄像跨语性转移的学习分解语义表示
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension
论文作者
论文摘要
多语言预训练的模型能够在机器阅读理解（MRC）中从丰富的资源到低资源语言零转移知识（MRC）。但是，不同语言的固有语言差异可能会使零射传递所预测的答案跨度违反了目标语言的句法约束。在本文中，我们提出了一个新型的多语言MRC框架，该框架配备了暹罗语义分解模型（SSDM），以通过多种语言预训练的模型学到的表示，以将语义与语法分离。为了明确地将语义知识转移到目标语言中，我们提出了针对语义和句法编码和解开的两组损失。对三个多语言MRC数据集（即Xquad，MLQA和Tydi QA）的实验结果证明了我们提出的方法对基于Mbert和XLM-100的模型的有效性。代码可在以下网址提供：https：//github.com/wulinjuan/ssdm_mrc。
Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.
