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.

[Q]: E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process.  The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets.  
 Question: What non-annotated datasets are considered?
[A]: E2E NLG challenge Dataset The Wikipedia Company Dataset


[Q]: The annotator carried out all annotation. 
 Question: How many annotators tagged each tweet?
[A]: One


[Q]: To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem. 
 Question: What are overall baseline results on new this new task?
[A]:
all of our models outperform the random baseline by a wide margin he best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116)