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.

Q: To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place. For machine translation, Google translation API is used.
Question: how did the authors translate the reviews to other languages?

A: Using Google translation API.
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Q: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement)
Question: Based on this paper, what is the more predictive set of features to detect fake news?

A: words embeddings, style, and morality features
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Q: We use the Universal Dependencies' Hindi-English codemixed data set BIBREF9 to test the model's ability to label code-mixed data. This dataset is based on code-switching tweets of Hindi and English multilingual speakers. We use the Devanagari script provided by the data set as input tokens.
Question: What codemixed language pairs are evaluated?

A:
Hindi-English
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