Much recent attention has been devoted to analyzing  sentence representations learned by neural encoders, through the paradigm of ' probing' tasks. This is often motivated by an interest to understand the information a model uses to make its decision. However, to what extent is the information encoded in a sentence representation actually used for the task which the encoder is trained on ? In this work, we examine this probing paradigm through a case-study in Natural Language Inference, showing that models  learn to encode linguistic properties even when not needed for a task . We identify that pre-trained 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. Through a set of controlled synthetic tasks, we demonstrate models can encode these properties considerably above chance-level even when distributed  as random noise, calling into question the interpretation of absolute claims on probing tasks.
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.
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