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

[Q]: Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.
Question: By how much does their method outperform state-of-the-art OOD detection?
[A]: AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average

[Q]: For the datasets, we use Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10 .
Question: What dataset/corpus is this work evaluated over?
[A]:
Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10