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.

[EX Q]: Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order.
Question: Do they impose any grammatical constraints over the generated output?
[EX A]: No

[EX Q]: The choice of two methods for our empirical study is motivated by the best performance achieved by Logistic Regression in question-question similarity at SemEval 2017 (best system BIBREF37 and second best system BIBREF38 ), and the high performance achieved by neural networks on larger datasets such as SNLI BIBREF13 , BIBREF39 , BIBREF40 , BIBREF41
Question: What machine learning and deep learning methods are used for RQE?
[EX A]: Logistic Regression neural networks

[EX Q]: Plackett-Luce Model for SMT Reranking
After being de-duplicated, the N-best list has an average size of around 300, and with 7491 features. This experiment displays, in large-scale features, the Plackett-Luce model correlates with BLEU score very well, and alleviates overfitting in some degree.
Question: What experiments with large-scale features are performed?
[EX A]:
Plackett-Luce Model for SMT Reranking