论文标题
探测探测范式：探测准确性是否需要任务相关性？
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?
论文作者
论文摘要
尽管神经模型在几个NLP基准上取得了令人印象深刻的结果，但对他们执行语言任务的机制几乎没有理解。因此，最近的许多关注都致力于分析神经编码者通过“探测”任务的镜头所学到的句子表示。但是，如通过探针发现的句子表示中编码的信息在多大程度上是模型执行其任务的探针的？在这项工作中，我们通过自然语言推断的案例研究检查了这种探测范式，表明即使不需要对模型的任务进行培训的任务，模型也可以学会编码语言属性。我们进一步确定，预算的单词嵌入在编码这些属性而不是训练任务本身中起着重要作用，从而强调了设计探测实验时仔细控制的重要性。最后，通过一组受控的合成任务，我们演示模型即使在数据中分布在数据中，也可以将这些属性大大地高于机会级别，从而质疑对探测任务的绝对主张的解释。
Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks. Thus, much recent attention has been devoted to analyzing the sentence representations learned by neural encoders, through the lens of `probing' tasks. However, to what extent was the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task? In this work, we examine this probing paradigm through a case study in Natural Language Inference, showing that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained. We further identify that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself, highlighting the importance of careful controls when designing probing experiments. Finally, through a set of controlled synthetic tasks, we demonstrate models can encode these properties considerably above chance-level even when distributed in the data as random noise, calling into question the interpretation of absolute claims on probing tasks.
