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.

[EX Q]: They can answer each question with either 'yes', 'rather yes', 'rather no', or 'no'. They can supplement each answer with a comment of at most 500 characters.
Question: What annotations are present in dataset?
[EX A]: answer each question with either 'yes', 'rather yes', 'rather no', or 'no'. can supplement each answer with a comment of at most 500 characters

[EX Q]: 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?
[EX A]: Yes

[EX Q]: To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training.
Question: What are the performance results?
[EX A]:
results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively.