  We present the TAPAS contribution to the Shared Task on Statement
Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al.
(2021)). SEM TAB FACT Task A is a classification task of recognizing if a
statement is entailed, neutral or refuted by the content of a given table. We
adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task. We
learn two binary classification models: A first model to predict if a statement
is neutral or non-neutral and a second one to predict if it is entailed or
refuted. As the shared task training set contains only entailed or refuted
examples, we generate artificial neutral examples to train the first model.
Both models are pre-trained using a MASKLM objective, intermediate
counter-factual and synthetic data (Eisenschlos et al., 2020) and TABFACT (Chen
et al., 2020), a large table entailment dataset. We find that the artificial
neutral examples are somewhat effective at training the first model, achieving
68.03 test F1 versus the 60.47 of a majority baseline. For the second stage, we
find that the pre-training on the intermediate data and TABFACT improves the
results over MASKLM pre-training (68.03 vs 57.01).
