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.
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Question: This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion 
 Question: What are the six target languages?

Answer: Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).


Question: While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort. 
 Question: What language is the experiment done in?

Answer: english language


Question:  In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training Sarcasm Datasets Used in the Experiment
This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.

Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.

We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics. 
 Question: Which benchmark datasets are used?

Answer:
Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset   dataset was created by BIBREF8  English dataset from BIBREF8  dataset from The Sarcasm Detector