TASK DEFINITION: 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.
PROBLEM: We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA.
Question: What is the underlying question answering algorithm?

SOLUTION: The system extends BiDAF BIBREF4 with self-attention

PROBLEM: Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary.
Question: What different correlations result when using different variants of ROUGE scores?

SOLUTION: we observe that many variants of Rouge scores do not have high correlations with human pyramid scores

PROBLEM: We evaluate LinkNBed and baselines on two real world knowledge graphs: D-IMDB (derived from large scale IMDB data snapshot) and D-FB (derived from large scale Freebase data snapshot).
Question: On what data is the model evaluated?

SOLUTION:
D-IMDB (derived from large scale IMDB data snapshot) D-FB (derived from large scale Freebase data snapshot)