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.

Example Input: In this paper, we have shown that both human and machine translation can alter superficial patterns in data, which requires reconsidering previous findings in cross-lingual transfer learning.
Question: Does the professional translation or the machine translation introduce the artifacts?
Example Output: Yes

Example Input: We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values.
Question: What new model is proposed for binomial lists?
Example Output: null model

Example Input: It is an order of magnitude more efficient in terms of training time. The model is complex, both in terms of implementation and run-time. Indeed, this model requires pre-training and mutual-learning and requires days of training time, whereas the simple architecture we propose requires on the order of an hour (and is easy to implement). MGNC-CNN is usually better than MG-CNN. And on the Subj dataset, MG-CNN actually achieves slightly better results than BIBREF11 , with far less complexity and required training time (MGNC-CNN performs comparably, although no better, here).
Question: How much faster is training time for MGNC-CNN over the baselines?
Example Output:
It is an order of magnitude more efficient in terms of training time. his model requires pre-training and mutual-learning and requires days of training time, whereas the simple architecture we propose requires on the order of an hour