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]: In Section SECREF16, we first provide more details about the experimental setting that we followed. As explained in Section SECREF3, we used BabelNet BIBREF29 as our reference taxonomy. BabelNet is a large-scale full-fledged taxonomy consisting of heterogeneous sources such as WordNet BIBREF36, Wikidata BIBREF37 and WiBi BIBREF38, making it suitable to test our hypothesis in a general setting. To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing. To tune the prior probability $\lambda _A$ for these categories, we hold out 10% from the training set as a validation set.
Question: What experiments they perform to demonstrate that their approach leads more accurate region based representations?
[A]: To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing.

[Q]: We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.
Question: what language pairs are explored?
[A]: German-English Turkish-English English-German

[Q]: Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts. We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions).
Question: How was a quality control performed so that the text is noisy but the annotations are accurate?
[A]:
The authors believe that the Wikilinks corpus contains ground truth annotations while being noisy. They discard mentions that cannot have ground-truth verified by comparison with Wikipedia.