TASK DEFINITION: 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.
PROBLEM: We conduct various experiments to illustrate the properties that are encouraged via different KL magnitudes. In particular, we revisit the interdependence between rate and distortion, and shed light on the impact of KL on the sharpness of the approximated posteriors. Then, through a set of qualitative and quantitative experiments for text generation, we demonstrate how certain generative behaviours could be imposed on VAEs via a range of maximum channel capacities. Finally, we run some experiments to find if any form of syntactic information is encoded in the latent space.

SOLUTION: What different properties of the posterior distribution are explored in the paper?

PROBLEM: Paraphrase Identification (PI) is the task of determining whether two sentences are paraphrase or not. We have implemented the cross-lingual variant of kernel functions for PI and RE tasks as described in section SECREF3 and measured the accuracy of models by testing them on the parallel data set.

SOLUTION: What classification task was used to evaluate the cross-lingual adaptation method described in this work?

PROBLEM: we take the CORD-19 dataset BIBREF2, which contains over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. We develop sentence classification methods to identify all sentences narrating radiological findings from COVID-19. We conduct experiments to verify the effectiveness of our method. From the CORD-19 dataset, our method successfully discovers a set of clinical findings that are closely related with COVID-19.

SOLUTION:
How large is the collection of COVID-19 literature?