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]: We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks. 
 Question: What normalization techniques are mentioned?
[A]: FBanks with cepstral mean normalization (CMN) variance with mean normalization (CMVN)


[Q]: We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords "vegan", "yoga", "job", "every_woman" having the highest term frequency. We can infer different things from the topic that "women usually practice yoga more than men", "women teach yoga and take it as a job", "Yogi follow vegan diet". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'. Women-Yoga 
 Question: What other interesting correlations are observed?
[A]: Women-Yoga


[Q]: Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities.  
 Question: How large is the dataset?
[A]:
6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities