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We present a large-scale empirical investigation of the zero-shot learningphenomena in a specific recognizing textual entailment (RTE) task category,i.e. the automated mining of leaderboards for Empirical AI Research. The priorreported state-of-the-art models for leaderboards extraction formulated as anRTE task, in a non-zero-shot setting, are promising with above 90% reportedperformances. However, a central research question remains unexamined: did themodels actually learn entailment? Thus, for the experiments in this paper, twoprior reported state-of-the-art models are tested out-of-the-box for theirability to generalize or their capacity for entailment, given leaderboardlabels that were unseen during training. We hypothesize that if the modelslearned entailment, their zero-shot performances can be expected to bemoderately high as well--perhaps, concretely, better than chance. As a resultof this work, a zero-shot labeled dataset is created via distant labelingformulating the leaderboard extraction RTE task.

Zero-shot Entailment of Leaderboards for Empirical AI Research