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]: In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it. We also use interval segment embeddings to distinguish multiple sentences within a document. This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse. Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder. 
 Question: What is novel about their document-level encoder?
[EX A]: Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it document representations are learned hierarchically

[EX Q]: In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers. 
 Question: Which framework they propose in this paper?
[EX A]:  extraction-then-synthesis framework

[EX Q]: For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs. 
 Question: Which NMT models did they experiment with?
[EX A]:
2-layer LSTM model with 500 hidden units in both encoder and decoder