In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: The theorem is proven constructively by selecting the parameters of the multi-head self-attention layer so that the latter acts like a convolutional layer.

Answer: How they prove that multi-head self-attention is at least as powerful as convolution layer?

Question: Our official scores (column Ens Test in Table TABREF19 ) have placed us second (EI-Reg, EI-Oc), fourth (V-Reg) and fifth (V-Oc) on the SemEval AIT-2018 leaderboard.

Answer: What were the scores of their system?

Question: In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction. We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset. For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset position-aware neural sequence model.

Answer:
Which competitive relational classification models do they test?