深度流形学习用于带Polytuplet损失的阅读理解与逻辑推理任务
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss
AI总结：
本文提出polytuplet损失函数及配套模型架构，通过强调相对准确率策略，在ReClor数据集上提升阅读理解与逻辑推理任务的性能，并优于基线模型。
摘要：
当前开发用于阅读理解与逻辑推理任务的机器学习模型的趋势，聚焦于提升模型理解和运用逻辑规则的能力。本研究致力于提供一种新颖的损失函数及配套的模型架构，通过表征人类在完成阅读理解与逻辑推理任务时常用的一种策略，使其比某些其他模型具有更多可解释的组件。我们的策略强调相对准确率而非绝对准确率，并能在知识不完整的情况下理论上得出正确答案。我们检验了该策略在解决阅读理解与逻辑推理问题上的有效性。模型在ReClor数据集上进行了评估，该数据集是一个具有挑战性的阅读理解与逻辑推理基准。我们提出了polytuplet损失函数，该函数强制优先学习答案选项的相对正确性，而非学习每个选项的真实准确率。我们的结果表明，采用polytuplet损失的模型优于现有基线模型，尽管仍需进一步研究以量化其可能带来的益处。
英文摘要：
The current trend in developing machine learning models for reading comprehension and logical reasoning tasks is focused on improving the models' abilities to understand and utilize logical rules. This work focuses on providing a novel loss function and accompanying model architecture that has more interpretable components than some other models by representing a common strategy employed by humans when given reading comprehension and logical reasoning tasks. Our strategy involves emphasizing relative accuracy over absolute accuracy and can theoretically produce the correct answer with incomplete knowledge. We examine the effectiveness of this strategy to solve reading comprehension and logical reasoning questions. The models were evaluated on the ReClor dataset, a challenging reading comprehension and logical reasoning benchmark. We propose the polytuplet loss function, which forces prioritization of learning the relative correctness of answer choices over learning the true accuracy of each choice. Our results indicate that models employing polytuplet loss outperform existing baseline models, though further research is required to quantify the benefits it may present.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
