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How Can Self-Attention Networks Recognize Dyck-n Languages?
For $\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences whereas the accuracy of SA$^+$ is 58. 82$\%$.
Towards a Flexible Embedding Learning Framework
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
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