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.

Example Input: if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Here, we demonstrate another potential use of adversarial modifications: finding erroneous triples in the knowledge graph. Intuitively, if there is an error in the graph, the triple is likely to be inconsistent with its neighborhood, and thus the model should put least trust on this triple. In other words, the error triple should have the least influence on the model's prediction of the training data. Formally, to find the incorrect triple $\langle s^{\prime }, r^{\prime }, o\rangle $ in the neighborhood of the train triple $\langle s, r, o\rangle $ , we need to find the triple $\langle s^{\prime },r^{\prime },o\rangle $ that results in the least change $\Delta _{(s^{\prime },r^{\prime })}(s,r,o)$ when removed from the graph.
Example Output: How is this approach used to detect incorrect facts?

Example Input: The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence.  The baseline for the FLC task generates spans and selects one of the 18 techniques randomly. 
Example Output: What was the baseline for this task?

Example Input: Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5 
Example Output:
How was the training data translated?

