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:this https URL.

本文提出一种新颖的多语言机器阅读理解框架，配备 Siamese Semantic Disentanglement Model（SSDM），用于在多语言预训练模型中学习的表示中分离语义和句法，以明确将语义知识转移到目标语言；实验证明，该方法比基于 mBERT 和 XLM-100 的模型更为有效。

学习语义解耦表示以实现多语言机器阅读理解中的零样本跨语言转移

Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

Coreference resolution is essential for natural language understanding and
has been long studied in NLP. In recent years, as the format of Question
Answering (QA) became a standard for machine reading comprehension (MRC), there
have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to
evaluate the ability of MRC models to reason about coreference. However, as we
show, coreference reasoning in MRC is a greater challenge than earlier thought;
MRC datasets do not reflect the natural distribution and, consequently, the
challenges of coreference reasoning. Specifically, success on these datasets
does not reflect a model's proficiency in coreference reasoning. We propose a
methodology for creating MRC datasets that better reflect the challenges of
coreference reasoning and use it to create a sample evaluation set. The results
on our dataset show that state-of-the-art models still struggle with these
phenomena. Furthermore, we develop an effective way to use naturally occurring
coreference phenomena from existing coreference resolution datasets when
training MRC models. This allows us to show an improvement in the coreference
reasoning abilities of state-of-the-art models. The code and the resulting
dataset are available at this https URL

本文认为使用 MRC 数据集评估 coreference reasoning 能力是有限的，提出了更能反映 coreference reasoning 挑战的 MRC 数据集构建方法，并使用先前 coreference resolution 数据集中的自然现象，展示了训练 MRC 模型的有效方式，并取得了 state-of-the-art models 的改进。

机器阅读理解中的共指推理

Coreference Reasoning in Machine Reading Comprehension

Remarkable success has been achieved in the last few years on some limited
machine reading comprehension (MRC) tasks. However, it is still difficult to
interpret the predictions of existing MRC models. In this paper, we focus on
extracting evidence sentences that can explain or support the answers of
multiple-choice MRC tasks, where the majority of answer options cannot be
directly extracted from reference documents.
Due to the lack of ground truth evidence sentence labels in most cases, we
apply distant supervision to generate imperfect labels and then use them to
train an evidence sentence extractor. To denoise the noisy labels, we apply a
recently proposed deep probabilistic logic learning framework to incorporate
both sentence-level and cross-sentence linguistic indicators for indirect
supervision. We feed the extracted evidence sentences into existing MRC models
and evaluate the end-to-end performance on three challenging multiple-choice
MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better
performance than the same models that take as input the full reference
document. To the best of our knowledge, this is the first work extracting
evidence sentences for multiple-choice MRC.

本研究提出一种利用深度概率逻辑学习框架提取证据句子的方法，在三个具有挑战性的多项选择机器阅读理解数据集上实现了可比较或更好的性能，为多项选择机器阅读理解提供了新的思路。