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: 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?

A: 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
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Q: Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. 
 Question: Do the authors mention any confounds to their study?

A: No
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Q: For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training. 
 Question: By how much do they outperform basic greedy and cross-entropy beam decoding?

A:
2 accuracy points
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