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.

[EX Q]: Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence.
Question: Do the decoder LSTMs all have the same weights?
[EX A]: No

[EX Q]: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks.
Question: For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo's embedding?
[EX A]: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks

[EX Q]: In almost all genres, DenseNMT models are significantly better than the baselines.
Question: did they outperform previous methods?
[EX A]:
Yes