TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: The multi-task model outperforms the single-task model at all data sizes, and the relative performance increases as the size of the training data decreases. When only 200 sentences of training data are used, the performance of the multi-task model is about 60% better than the single-task model for both the Airbnb and Greyhound apps. The relative gain for the OpenTable app is 26%. The results differ between the tasks, but none have an overall benefit from the open vocabulary system.

SOLUTION: Does the performance increase using their method?

PROBLEM: Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma. As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA.

SOLUTION: What is the state of the art?

PROBLEM: 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)

SOLUTION:
Based on this paper, what is the more predictive set of features to detect fake news?