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]: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., "If a surface has more friction, then an object will travel slower"), and asked to enter two objects, people, or situations to compare.
Question: Do all questions in the dataset allow the answers to pick from 2 options?
[EX A]: Yes

[EX Q]: Our training data consists of 2.09M sentence pairs extracted from LDC corpus. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set.
Question: What dataset did they use?
[EX A]: LDC corpus NIST 2003(MT03) NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) NIST 2008(MT08)

[EX Q]: We show that by determining and integrating heterogeneous set of features from different modalities aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
Question: What is the source of the user interaction data?
[EX A]:
Sociability from ego-network on Twitter