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:
Compared to the in-context-parsing-based system, the combination with exact string matching yields a gain in recall of over 6%, and the combination with inflectional string matching yields an even bigger gain of almost 8%, at precision losses of 0.6% and 0.8%, respectively. 
 Question: What compleentary PIE extraction methods are used to increase reliability further?
answer:
exact string matching inflectional string matching


question:
27,534 messages were left after filtering. This is the data set that is used for experimentation. 
 Question: What is the size of the labelled dataset?
answer:

27,534 messages