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.

Example Input: Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence. 
 Question: Do the decoder LSTMs all have the same weights?
Example Output: No

Example Input: 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?
Example Output: using topic modeling model Latent Dirichlet Allocation (LDA)

Example Input: We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2.  Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels. 
 Question: what experiments are conducted?
Example Output:
a reliability study for the proposed scheme