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.

[Q]: We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6.
[A]: What state of the art models were used in experiments?


[Q]: Sentiment Classification
We first conduct a multi-task experiment on sentiment classification.

We use 16 different datasets from several popular review corpora used in BIBREF20 . These datasets consist of 14 product review datasets and two movie review datasets.

All the datasets in each task are partitioned randomly into training set, development set and testing set with the proportion of 70%, 10% and 20% respectively. Transferability of Shared Sentence Representation
With attention mechanism, the shared sentence encoder in our proposed models can generate more generic task-invariant representations, which can be considered as off-the-shelf knowledge and then be used for unseen new tasks. Introducing Sequence Labeling as Auxiliary Task
A good sentence representation should include its linguistic information. Therefore, we incorporate sequence labeling task (such as POS Tagging and Chunking) as an auxiliary task into the multi-task learning framework, which is trained jointly with the primary tasks (the above 16 tasks of sentiment classification).
[A]: What tasks did they experiment with?


[Q]: We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 .
[A]:
On which dataset is the experiment conducted?