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:
To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?
answer:
hundreds of thousands of legal agreements


question:
The above pseudocode is agnostic with respect to the choice of fragmentation and environment functions; task-specific choices are described in more detail for each experiment below.

Discussion 
 Question: Which languages do they test on?
answer:

Answer with content missing: (Applications section) We use Wikipedia articles
in five languages
(Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English) as well as the Na dataset of Adams
et al. (2017).
Select:
Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English