In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

We compare our HR-VAE model with three strong baselines using VAE for text modelling:

VAE-LSTM-base: A variational autoencoder model which uses LSTM for both encoder and decoder. KL annealing is used to tackled the latent variable collapse issue BIBREF0;

VAE-CNN: A variational autoencoder model with a LSTM encoder and a dilated CNN decoder BIBREF7;

vMF-VAE: A variational autoencoder model using LSTM for both encoder and decoder where the prior distribution is the von Mises-Fisher (vMF) distribution rather than a Gaussian distribution BIBREF5.
Question: Do they compare against state of the art text generation?
Yes

A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12.
Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?
long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach

We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length.
Question: Do they measure how well they perform on longer sequences specifically?
Yes