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.

Ex Input:
In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.

Ex Output:
Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?


Ex Input:
For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. 

Ex Output:
How many paraphrases are generated per question?


Ex Input:
Given a set of evaluation documents, each having a known correct label from a closed set of labels (often referred to as the “gold-standard”), and a predicted label for each document from the same set, the document-level accuracy is the proportion of documents that are correctly labeled over the entire evaluation collection. This is the most frequently reported metric and conveys the same information as the error rate, which is simply the proportion of documents that are incorrectly labeled (i.e. INLINEFORM0 ). There are two distinct ways in which results are generally summarized per-language: (1) precision, in which documents are grouped according to their predicted language; and (2) recall, in which documents are grouped according to what language they are actually written in. It is also common practice to report an F-score INLINEFORM0 , which is the harmonic mean of precision and recall.

Ex Output:

what evaluation methods are discussed?