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.
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Question: First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K.
Question: What three datasets are used to measure performance?

Answer: FB24K DBP24K Game30K

Question: All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end.
Question: At what text unit/level were documents processed?

Answer: documents are segmented into paragraphs and processed at the paragraph level

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

Answer:
Yes