论文标题
超越句子级级自然语言推断下游任务
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
论文作者
论文摘要
近年来，自然语言推断（NLI）任务引起了人们的重大关注，新的数据集和模型在其上取得了近乎人为的表现。但是，尚未实现NLI的全部承诺 - 特别是它学习了应该推广到其他下游NLP任务的知识 - 尚未实现。在本文中，我们从两个下游任务的镜头中研究了这种未实现的承诺：问题回答（QA）和文本摘要。我们猜想NLI数据集和这些下游任务之间的关键区别涉及前提的长度。而且，从现有质量检查数据集中创建新的长前提NLI数据集是培训真正可推广的NLI模型的有前途的途径。我们通过在质量保证的任务上显示竞争成果，并获得有关检查摘要的事实正确性的最佳结果，来验证我们的猜想。
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it. However, the full promise of NLI -- particularly that it learns knowledge that should be generalizable to other downstream NLP tasks -- has not been realized. In this paper, we study this unfulfilled promise from the lens of two downstream tasks: question answering (QA), and text summarization. We conjecture that a key difference between the NLI datasets and these downstream tasks concerns the length of the premise; and that creating new long premise NLI datasets out of existing QA datasets is a promising avenue for training a truly generalizable NLI model. We validate our conjecture by showing competitive results on the task of QA and obtaining the best reported results on the task of Checking Factual Correctness of Summaries.
