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: Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively.

SOLUTION: What is the best model?

PROBLEM: For training data, 3 million webpages from this corpus were processed with a CCG parser to produce logical forms BIBREF10 . We also used the test set created by Krishnamurthy and Mitchell, which contains 220 queries generated in the same fashion as the training data from a separate section of ClueWeb. However, as they did not release a development set with their data, we used this set as a development set. This final test set contains 307 queries.

SOLUTION: How big is their dataset?

PROBLEM: Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.

SOLUTION:
What metrics are used to benchmark the results?