instruction:
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.
question:
Building on these results, we take a somewhat more systematic approach to looking for interpretable hidden state dimensions, by using decision trees to predict individual hidden state dimensions (Figure 2 ). We visualize the overall dynamics of the hidden states by coloring the training data with the k-means clusters on the state vectors (Figures 3 , 3 ). In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. 
 Question: Which methods do the authors use to reach the conclusion that LSTMs and HMMs learn complementary information?
answer:
decision trees to predict individual hidden state dimensions apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters


question:
The previously mentioned datasets are all in English 
 Question: For which languages most of the existing MRC datasets are created?
answer:
English


question:
The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples.  
 Question: What Named Entity Recognition dataset is used?
answer:

Groningen Meaning Bank