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.

Input: Consider Input: 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?

Output: E2E NLG challenge Dataset The Wikipedia Company Dataset

Input: Consider Input: We compare our personalized models against two baselines. The first is a name-based Nearest-Neighbor model (NN). We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity.
Question: What are the baseline models?

Output: name-based Nearest-Neighbor model (NN) Encoder-Decoder baseline with ingredient attention (Enc-Dec)

Input: Consider Input: The crowdworkers were located in the US and hired on the BIBREF22 platform.
Question: Who are the crowdworkers?
Output: people in the US that use Amazon Mechanical Turk