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.

Q: We have observed that the conflict model is very sensitive to even minor differences and compensates in such cases where attention poses high bias towards similarities already there in the sequences.

Sequence 1: What are the best ways to learn French ?

Sequence 2: How do I learn french genders ?

Attention only: 1

Attention+Conflict: 0

Ground Truth: 0

Sequence 1: How do I prevent breast cancer ?

Sequence 2: Is breast cancer preventable ?

Attention only: 1

Attention+Conflict: 0

Ground Truth: 0

We provide two examples with predictions from the models with only attention and combination of attention and conflict. Each example is accompanied by the ground truth in our data.

A: Do they show on which examples how conflict works better than attention?
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Q: GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias. In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied. We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other. Our methods are also evaluated on datasets with different unbalanced class distributions. We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents. Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive.

A: How do they define robustness of a model?
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Q: The News Category Dataset BIBREF11 is a collection of headlines published by HuffPost BIBREF12 between 2012 and 2018, and was obtained online from Kaggle BIBREF13. The full dataset contains 200k news headlines with category labels, publication dates, and short text descriptions.

A:
What news dataset was used?
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