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:
We evaluate with all commonly-used metrics in question generation BIBREF13: BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19. We use the evaluation script released by Chen2015MicrosoftCC.

Ex Output:
What metrics do they use?

Ex Input:
While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.

Ex Output:
How do you find the entity descriptions?

Ex Input:
Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP. To verify this, we experiment with translating the sentences with the following variations: No PrefixThe baseline translation as returned by the GMT system. "He said:"Signaling a male speaker. We expect to further skew the system towards masculine forms. "She said:"Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. "I said to them:"Signaling an unknown speaker and plural audience. "He said to them:"Masculine speaker and plural audience. "She said to them:"Female speaker and plural audiencethe complete, correct condition. We expect the best translation accuracy on this setup. "He/she said to him/her"Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology.

Ex Output:
What are the components of the black-box context injection system?