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.
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Question: Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported.
Question: Does pre-training on general text corpus improve performance?

Answer: No

Question: . Grammatically correct text is needed as the starting point for inserting artificial errors, and we used two different sources: 1) the corrected version of the same FCE training set on which the system is trained (450K tokens), and 2) example sentences extracted from the English Vocabulary Profile (270K tokens).
Question: Which languages are explored in this paper?

Answer: English

Question: Our system learns Perceptron models BIBREF37 using the Machine Learning machinery provided by the Apache OpenNLP project with our own customized (local and clustering) features. The local features constitute our baseline system on top of which the clustering features are added.
Question: what are the baselines?

Answer:
Perceptron model using the local features.