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: In our methodology to design new evaluation metrics for comparing reference summaries/translations to hypothesis ones, we established first-principles criteria on what a good evaluator should do. The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences. 
 Question: What is the criteria for a good metric?

SOLUTION: The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.

PROBLEM: In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?

SOLUTION: using word2vec to create features that are used as input to the SVM

PROBLEM: Let us take a closer look at what we mean by agreement between the two parse trees. Essentially, if we have two words in the English sentence denoted by i and i', aligned to words j and j' in the parallel Hindi sentence respectively, we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree, and vice versa. For an edge in the English parse tree, we term the corresponding edge or path in the Hindi parse tree as the projection or projected path of the English edge on the Hindi parse tree, and similarly there are projected paths from Hindi to English. The dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints. 
 Question: How does enforcing agreement between parse trees work across different languages?

SOLUTION:
we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints