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: The average classification accuracy results are summarised in Table TABREF9.
Example Output: What evaluation metric is used?

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: To counter that, we use a left-to-right attention mask, similar to the one employed in the original Transformer decoder BIBREF1. For the input tokens in $X$, we apply such mask to all the target tokens $Y$ that were concatenated to $X$, so that input tokens can only attend to the other input tokens. Conversely, for target tokens $y_t$, we put an attention mask on all tokens $y_{>t}$, allowing target tokens $y_t$ to attend only to the input tokens and the already generated target tokens.
Example Output:
What is different in BERT-gen from standard BERT?

