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.

Input: Consider Input: Hypothesis 4 (H4) : The proposed template-based synthesis model outperforms a simple word replacement model. Table TABREF13 supports H4 by showing that the proposed synthesis model outperforms the WordNet baseline in training (rows 7, 8 and 19, 20) except Stat2016, and tuning (10, 11 and 22, 23) over all courses. It also shows that while adding synthetic data from the baseline is not always helpful, adding synthetic data from the template model helps to improve both the training and the tuning process. In both CS and ENGR courses, tuning with synthetic data enhances all ROUGE scores compared to tuning with only the original data. (rows 9 and 11). As for Stat2015, R-1 and R-$L$ improved, while R-2 decreased. For Stat2016, R-2 and R-$L$ improved, and R-1 decreased (rows 21 and 23). Training with both student reflection data and synthetic data compared to training with only student reflection data yields similar improvements, supporting H3 (rows 6, 8 and 18, 20).
Question: Is the template-based model realistic?

Output: Yes

Input: Consider Input: 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?

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

Input: Consider Input: The most intuitive way to represent syntactic information is to use individual dependency relations directly, like dependency head and dependency relation label, denoted as Dep and Rel for short. In order to preserve the structural information of dependency trees as much as possible, we take the syntactic path between candidate arguments and predicates in dependency trees as linguistic knowledge. Referring to BIBREF9, we use the Tree-based Position Feature (TPF) as Dependency Path (DepPath) and use the Shortest Dependency Path (SDP) as Relation Path (RelPath).
Question: What different approaches of encoding syntactic information authors present?
Output: dependency head and dependency relation label, denoted as Dep and Rel for short Tree-based Position Feature (TPF) as Dependency Path (DepPath) Shortest Dependency Path (SDP) as Relation Path (RelPath)