instruction:
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.
question:
Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively.
answer:
What is the best model?


question:
To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0.
answer:
Do they evaluate on NER data sets?


question:
We consider two types of architectures: a bi-directional language model to augment the sequence to sequence encoder and a uni-directional model to augment the decoder. Both use self-attention BIBREF16 and the uni-directional model contains INLINEFORM0 transformer blocks, followed by a word classifier to predict the next word on the right. bi-directional language model to augment the sequence to sequence encoder
answer:

What language model architectures are used?