论文标题
通过变压器语言模型简化段落级问题的生成
Simplifying Paragraph-level Question Generation via Transformer Language Models
论文作者
论文摘要
问题生成（QG）是一项自然语言生成任务，在其中训练模型以提出与某些输入文本相对应的问题。最近的方法将QG作为序列到序列问题构架，并依靠其他功能和机制来提高性能；但是，这些通常会增加模型的复杂性，并且可以依靠辅助数据在实际使用中不可用。利用转移学习的基于单向的单向语言模型可用于在处理其他特定于任务的复杂性的同时产生高质量的问题。我们的QG型号从GPT-2小型的固定量较小，在小队数据集上的段落级质量级基准的表现优于0.95 Meteor Points。人类评估者将问题评为易于回答，与他们的上下文段落相关，并且与自然的人言语相对应。还引入了Race数据集上的一组新的基线分数，以前尚未用于QG任务。建议使用不同的模型能力和具有非识别类型问题的数据集进行进一步的实验，以进一步验证验证的基于变压器的LMS作为问题发生器的鲁棒性。
Question generation (QG) is a natural language generation task where a model is trained to ask questions corresponding to some input text. Most recent approaches frame QG as a sequence-to-sequence problem and rely on additional features and mechanisms to increase performance; however, these often increase model complexity, and can rely on auxiliary data unavailable in practical use. A single Transformer-based unidirectional language model leveraging transfer learning can be used to produce high quality questions while disposing of additional task-specific complexity. Our QG model, finetuned from GPT-2 Small, outperforms several paragraph-level QG baselines on the SQuAD dataset by 0.95 METEOR points. Human evaluators rated questions as easy to answer, relevant to their context paragraph, and corresponding well to natural human speech. Also introduced is a new set of baseline scores on the RACE dataset, which has not previously been used for QG tasks. Further experimentation with varying model capacities and datasets with non-identification type questions is recommended in order to further verify the robustness of pretrained Transformer-based LMs as question generators.
