In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied. 
 Question: Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
Yes-no

Although the competition proposes two different scenarios, in fact, both are guided by the snomed ct ontology —for subtask 1, entities must be identified with offsets and mapped to a predefined set of four classes (PROTEINAS, NORMALIZABLES, NO_NORMALIZABLES and UNCLEAR); for subtask 2, a list of all snomed ct ids (sctid) for entities occurring in the text must be given, which has been called concept indexing by the shared task organizers. 
 Question: What are the two PharmaCoNER subtasks?
Abstractive

For the language modeling evaluation, we also evaluate a baseline without knowledge distillation (termed NoKD), with a model parameterized identically to the distilled student models but trained directly on the teacher model objective from scratch. For downstream tasks, we compare with NoKD as well as Patient Knowledge Distillation (PKD) from BIBREF34, who distill the 12-layer BERTBASE model into 3 and 6-layer BERT models by using the teacher model's hidden states. 
 Question: What state-of-the-art compression techniques were used in the comparison?

Extractive