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.

Input: Consider Input: However, recent work has found that many NLI datasets contain biases, or annotation artifacts, i.e., features present in hypotheses that enable models to perform surprisingly well using only the hypothesis, without learning the relationship between two texts BIBREF2 , BIBREF3 , BIBREF4 . For instance, in some datasets, negation words like “not” and “nobody” are often associated with a relationship of contradiction. As a ramification of such biases, models may not generalize well to other datasets that contain different or no such biases.

Output: Is such bias caused by bad annotation?


Input: Consider Input: The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix.

Output: What evaluation metrics did they use?


Input: Consider Input: Moreover, to be able to make use of multiple answer styles within a single system, our model introduces an artificial token corresponding to the target style at the beginning of the answer sentence ( $y_1$ ), like BIBREF14 . At test time, the user can specify the first token to control the answer styles.


Output: Does their model also take the expected answer style as input?