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.

Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model.
Question: Which translation system do they use to translate to English?
Attention-based translation model with convolution sequence to sequence model

Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it "LEAD". The other system is called "QUERY_SIM", which directly ranks sentences according to its TF-IDF cosine similarity to the query. Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it "LEAD". The other system is called "QUERY_SIM", which directly ranks sentences according to its TF-IDF cosine similarity to the query. In addition, we implement two popular extractive query-focused summarization methods, called MultiMR BIBREF2 and SVR BIBREF20 . Since our model is totally data-driven, we introduce a recent summarization system DocEmb BIBREF9 that also just use deep neural network features to rank sentences. To verify the effectiveness of the joint model, we design a baseline called ISOLATION, which performs saliency ranking and relevance ranking in isolation.
Question: What models do they compare to?
LEAD QUERY_SIM MultiMR SVR DocEmb ISOLATION

Adding word alignments in parallel sentences results in small, non significant improvements, even if there is some labeled data available in the source language. This difficulty in showing the usefulness of parallel corpora for SRI may be due to the current assumptions about role alignments, which mean that only a small percentage of roles are aligned. Further analyses reveals that annotating small amounts of data can easily outperform the performance gains obtained by adding large unlabeled dataset as well as adding parallel corpora.
Question: Overall, does having parallel data improve semantic role induction across multiple languages?
No