TASK DEFINITION: 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.
PROBLEM: As we only extracted references to other judicial decisions, we obtained 471,319 references from Supreme Court decisions, 167,237 references from Supreme Administrative Court decisions and 264,463 references from Constitutional Court Decisions. These are numbers of text spans identified as references prior the further processing described in Section SECREF3.
Question: How big is the dataset?

SOLUTION: 903019 references

PROBLEM: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning "diabetes", "cancer", and "influenza" into a topic that has an overall "disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
Question: How were topics of interest about DDEO identified?

SOLUTION: using topic modeling model Latent Dirichlet Allocation (LDA)

PROBLEM: The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism.
Question: How many attention layers are there in their model?

SOLUTION:
one