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.
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Question: The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora.

Answer: Do they test their approach on a dataset without incomplete data?


Question: Before designing this NLP toolkit, we conducted a survey among engineers and identified a spectrum of three typical personas.

Answer: How do the authors evidence the claim that many engineers find it a big overhead to choose from multiple frameworks, models and optimization techniques?


Question: 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.

Answer:
How is this approach used to detect incorrect facts?