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]: 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.
[A]: Is machine learning system underneath similar to image caption ML systems?


[Q]: From the in-corpus experiments, we obtain good results on SarcasmCorpus, which is the only corpus containing Amazon reviews. Unfortunately, when we train our models in a cross-corpora or all-corpora setting, our results drop dramatically, especially in the cross-corpora case. These results mean that the sarcasm in SarcasmCorpus is conveyed through features that are not present in the other corpora.
[A]: In which domains is sarcasm conveyed in different ways?


[Q]: N-grams, POS, Hierarchical features: A baseline bag-of-words model incorporating both tagged and untagged unigrams and bigams.  CNN: Kim BIBREF28 demonstrated near state-of-the-art performance on a number of sentence classification tasks (including TREC question classification) by using pre-trained word embeddings BIBREF40 as feature extractors in a CNN model.
[A]:
What previous methods is their model compared to?