拥抱模糊性：改变NLI模型的培训目标
Embracing Ambiguity: Shifting the Training Target of NLI Models
摘要：
自然语言推理（NLI）数据集包含具有高度模糊标签的示例。虽然许多研究工作并没有注意到这一事实，但最近的一些努力已经承认并接受了歧义的存在，如UNLI和ChaosNLI。在本文中，我们探索了直接在NLI任务中的注释器的估计标签分布上进行训练的选项，使用基于这种模糊性分布的学习损失而不是黄金标签。我们准备了AmbiNLI，一个从现成来源获得的试验数据集，并表明在对这些数据进行微调时可以降低ChaosNLI分歧分数，这是学习如何捕捉语言歧义的有希望的第一步。此外，我们还表明，在相同数量的数据上进行训练，但以模糊度分布而不是黄金标签为目标，可以使模型实现更高的性能，并为下游任务学习更好的表示。
英文摘要：
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.
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。
