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.

Input: Consider Input: 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.

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


Input: Consider Input: In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:

we fully connect nodes that represent sentences from the same passage (dotted-black);

we fully connect nodes that represent the first sentence of each passage (dotted-red);

we add an edge between the question and every node for each passage (dotted-blue).

Output: How are some nodes initially connected based on text structure?


Input: Consider Input: Various automated evaluation approaches are proposed to facilitate the development and evaluation of NLG models. We summarize these evaluation approaches below. Text Overlap Metrics, including BLEU BIBREF5, METEOR BIBREF6 and ROUGE BIBREF7, are the most popular metrics employed in the evaluation of NLG models. Perplexity is commonly used to evaluate the quality of a language model. Parameterized Metrics learn a parameterized model to evaluate generated text.


Output: What previous automated evalution approaches authors mention?