A trending paradigm for multiple-choice question answering (MCQA) is using a
text-to-text framework. By unifying data in different tasks into a single
text-to-text format, it trains a generative encoder-decoder model which is both
powerful and universal. However, a side effect of twisting a generation target
to fit the classification nature of MCQA is the under-utilization of the
decoder and the knowledge that can be decoded. To exploit the generation
capability and underlying knowledge of a pre-trained encoder-decoder model, in
this paper, we propose a generation-enhanced MCQA model named GenMC. It
generates a clue from the question and then leverages the clue to enhance a
reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.

该论文介绍了一种基于文本生成模型的、通过生成提示信息来提高多项选择题的准确性的算法 GenMC，该算法在多项选择题上的表现优于传统文本生成模型。

线索优先：增强型多项选择题解答

Clues Before Answers: Generation-Enhanced Multiple-Choice QA

Generating radiology reports is time-consuming and requires extensive
expertise in practice. Therefore, reliable automatic radiology report
generation is highly desired to alleviate the workload. Although deep learning
techniques have been successfully applied to image classification and image
captioning tasks, radiology report generation remains challenging in regards to
understanding and linking complicated medical visual contents with accurate
natural language descriptions. In addition, the data scales of open-access
datasets that contain paired medical images and reports remain very limited. To
cope with these practical challenges, we propose a generative encoder-decoder
model and focus on chest x-ray images and reports with the following
improvements. First, we pretrain the encoder with a large number of chest x-ray
images to accurately recognize 14 common radiographic observations, while
taking advantage of the multi-view images by enforcing the cross-view
consistency. Second, we synthesize multi-view visual features based on a
sentence-level attention mechanism in a late fusion fashion. In addition, in
order to enrich the decoder with descriptive semantics and enforce the
correctness of the deterministic medical-related contents such as mentions of
organs or diagnoses, we extract medical concepts based on the radiology reports
in the training data and fine-tune the encoder to extract the most frequent
medical concepts from the x-ray images. Such concepts are fused with each
decoding step by a word-level attention model. The experimental results
conducted on the Indiana University Chest X-Ray dataset demonstrate that the
proposed model achieves the state-of-the-art performance compared with other
baseline approaches.

提出了一种基于深度学习技术的生成式编码解码模型，通过对大量胸部 X 光图像进行预先训练并提取医疗概念，结合词级别的注意力机制，成功生成精准较为自然的放射学报告。