In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[Q]: We consider QA PGNet and Multi-decoder QA PGNet with lookup table embedding as baseline models and improve on the baselines with other variations described below.
Question: What is the baseline?
[A]: QA PGNet Multi-decoder QA PGNet with lookup table embedding

[Q]: The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.
Question: What was their performance on the dataset?
[A]: accuracy of 82.6%

[Q]: Logician is trained under the attention-based sequence-to-sequence paradigm, with three mechanisms: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information.
Question: How is Logician different from traditional seq2seq models?
[A]:
restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information