instruction:
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.
question:
We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German.
Question: what language pairs are explored?
answer:
German-English Turkish-English English-German

question:
Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ). AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.
Question: By how much does their method outperform state-of-the-art OOD detection?
answer:
AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average

question:
We used the relation classification dataset of the SemEval 2010 task 8 BIBREF8 . It consists of sentences which have been manually labeled with 19 relations (9 directed relations and one artificial class Other). 8,000 sentences have been distributed as training set and 2,717 sentences served as test set.
Question: Which dataset do they train their models on?
answer:
relation classification dataset of the SemEval 2010 task 8