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]:  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?
[EX 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.

[EX Q]: For comparison purpose, we build two baseline systems for each direction: one is use the traditional phrase-based statistical machine translation (SMT), the other one is the NMT system.  
 Question: what was the baseline?
[EX A]: traditional phrase-based statistical machine translation (SMT) NMT system

[EX Q]: For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats.  We also consider the task of paraphrase detection. 
 Question: What end tasks do they evaluate on?
[EX A]:
Sentiment analysis and paraphrase detection under adversarial attacks