This paper studies the bias problem of multi-hop question answering models,
of answering correctly without correct reasoning. One way to robustify these
models is by supervising to not only answer right, but also with right
reasoning chains. An existing direction is to annotate reasoning chains to
train models, requiring expensive additional annotations. In contrast, we
propose a new approach to learn evidentiality, deciding whether the answer
prediction is supported by correct evidences, without such annotations.
Instead, we compare counterfactual changes in answer confidence with and
without evidence sentences, to generate "pseudo-evidentiality" annotations. We
validate our proposed model on an original set and challenge set in HotpotQA,
showing that our method is accurate and robust in multi-hop reasoning.

本文研究了多跳问答模型中的偏差问题，提出了一种不需要额外昂贵的注释即可学习证据性的新方法，在 HotpotQA 验证了该方法准确，鲁棒性强。

通过伪证据训练来增强多跳问答的鲁棒性

Robustifying Multi-hop QA through Pseudo-Evidentiality Training

Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or
unseen domain by projecting the image data and semantic labels into a joint
embedding space. However, most existing methods directly adapt a well-trained
projection from one domain to another, thereby ignoring the serious bias
problem caused by domain differences. To address this issue, we propose a novel
Domain-Specific Embedding Network (DSEN) that can apply specific projections to
different domains for unbiased embedding, as well as several domain
constraints. In contrast to previous methods, the DSEN decomposes the
domain-shared projection function into one domain-invariant and two
domain-specific sub-functions to explore the similarities and differences
between two domains. To prevent the two specific projections from breaking the
semantic relationship, a semantic reconstruction constraint is proposed by
applying the same decoder function to them in a cycle consistency way.
Furthermore, a domain division constraint is developed to directly penalize the
margin between real and pseudo image features in respective seen and unseen
domains, which can enlarge the inter-domain difference of visual features.
Extensive experiments on four public benchmarks demonstrate the effectiveness
of DSEN with an average of $9.2\%$ improvement in terms of harmonic mean. The
code is available in https://github.com/mboboGO/DSEN-for-GZSL.

提出了一种基于领域特定嵌入网络（DSEN）的零样本学习方法，通过将图像数据和语义标签投影到联合嵌入空间中进行识别，该方法分解了领域共享投影函数并使用领域特定子函数探索领域的相似性和差异性，同时引入语义重构和领域划分约束以解决偏倚问题和保持语义关联。实验表明，DSEN 在四个公共基准测试中具有显著的改进。