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.

Example Input: For comparison purpose, we build two baseline systems for each direction: one is use the traditional phrase-based statistical machine translation (SMT), the other one is the NMT system.
Question: what was the baseline?
Example Output: traditional phrase-based statistical machine translation (SMT) NMT system

Example Input: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets.
Question: What metrics are used for evaluation?
Example Output: ROUGE BIBREF29 and METEOR BIBREF30

Example Input: While the plan generation stage is guaranteed to be faithful to the input, the translation process from plans to text is based on a neural seq2seq model and may suffer from known issues with such models: hallucinating facts that do not exist in the input, repeating facts, or dropping facts. While the clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors of these kinds. Recent work in neural text generation and summarization attempt to address these issues by trying to map the textual outputs back to structured predicates, and comparing these predicates to the input data.
Question: What is the effectiveness plan generation?
Example Output:
clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors work in neural text generation and summarization attempt to address these issues