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.
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Question: We compared our multi-turn emotionally engaging dialog model (denoted as MEED) with two baselines—the vanilla sequence-to-sequence model (denoted as S2S) and HRAN.

Answer: What two baseline models are used?


Question: While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.

Answer: How do you find the entity descriptions?


Question: We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.

Answer:
Which baselines did they compare?