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Table 3: 

Statistics of SufficientFacts presenting the predictions of the models in the ensemble (Model Pred: Agree Enough Information (EI Agree), Agree Not Enough Information (NEI Agree), Disagree, and Total) vs human annotations of the same (EI – Irrelevant (EI_I), EI – Repeated (EI_R), NEI). We present sentence (SENT) and constituent omission (CONST) dataset splits separately. We embolden/underline results of the datasets for predictions where the three models agree (NEI Agree, EI Agree) and have the highest/lowest agreement with human annotations about EI_I, EI_R, and NEI predictions. We use / to denote where lower/higher results are better.

Statistics of SufficientFacts presenting the predictions of the models in the ensemble (Model Pred: Agree Enough Information (EI Agree), Agree Not Enough Information (NEI Agree), Disagree, and Total) vs human annotations of the same (EI – Irrelevant (EI_I), EI – Repeated (EI_R), NEI). We present sentence (SENT) and constituent omission (CONST) dataset splits separately. We embolden/underline results of the datasets for predictions where the three models agree (NEI Agree, EI Agree) and have the highest/lowest agreement with human annotations about EI_I, EI_R, and NEI predictions. We use / to denote where lower/higher results are better.
Statistics of SufficientFacts presenting the predictions of the models in the ensemble (Model Pred: Agree Enough Information (EI Agree), Agree Not Enough Information (NEI Agree), Disagree, and Total) vs human annotations of the same (EI – Irrelevant (EI_I), EI – Repeated (EI_R), NEI). We present sentence (SENT) and constituent omission (CONST) dataset splits separately. We embolden/underline results of the datasets for predictions where the three models agree (NEI Agree, EI Agree) and have the highest/lowest agreement with human annotations about EI_I, EI_R, and NEI predictions. We use / to denote where lower/higher results are better.
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