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: We employ two sources of e-book annotation data: (i) editor tags, and (ii) Amazon search terms. For editor tags, we collect data of 48,705 e-books from 13 publishers, namely Kunstmann, Delius-Klasnig, VUR, HJR, Diogenes, Campus, Kiwi, Beltz, Chbeck, Rowohlt, Droemer, Fischer and Neopubli. For the Amazon search terms, we collect search query logs of 21,243 e-books for 12 months (i.e., November 2017 to October 2018).
Question: what dataset was used?

Answer: 48,705 e-books from 13 publishers search query logs of 21,243 e-books for 12 months

Question: We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize the system to complete dialogs faster rather than slower, yielding return 0 for failed dialogs, and $G = 0.95^{T-1}$ for successful dialogs, where $T$ is the number of system turns in the dialog. 0.95^{T-1} reward 0.95^{T-1}
Question: What is the reward model for the reinforcement learning appraoch?

Answer: reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail

Question: In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:

we fully connect nodes that represent sentences from the same passage (dotted-black);

we fully connect nodes that represent the first sentence of each passage (dotted-red);

we add an edge between the question and every node for each passage (dotted-blue).
Question: How are some nodes initially connected based on text structure?

Answer:
we fully connect nodes that represent sentences from the same passage we fully connect nodes that represent the first sentence of each passage we add an edge between the question and every node for each passage