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: Our conversational agent uses two architectures to simulate a specialized reminiscence therapist. The block in charge of generating questions is based on the work Show, Attend and Tell BIBREF13. This work generates descriptions from pictures, also known as image captioning.

Answer: Is machine learning system underneath similar to image caption ML systems?


Question: Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing.

Answer: What is the architecture of their model?


Question: Language Model (LM): We train a two layer recurrent neural language model with GRU cells of hidden size 512.

 Sequence-to-Sequence Attention Model (S2S): We train a two layer neural sequence to sequence model equipped with bi-linear attention function with GRU cells of hidden size 512.  Linear Dynamical System (LDS): We also train a linear dynamical system as discussed in Section SECREF1 as one of our baselines for fair comparisons. Semi-Supervised SLDS (SLDS-X%): To gauge the usability of semi-supervision, we also train semi-supervised SLDS models with varying amount of labelled sentiment tags unlike the original model which uses 100% tagged data. 

Answer:
What baselines are used?