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: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed.
Question: Which matrix factorization methods do they use?

SOLUTION: weighted factorization of a word-context co-occurrence matrix

PROBLEM: replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer
Question: What changes they did on input module?

SOLUTION: For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer

PROBLEM: For the CNN-Dailymail dataset, the Lead-3 model is considered a strong baseline; both the abstractive BIBREF16 and extractive BIBREF14 state-of-the art methods on this dataset beat this baseline only marginally. For the proxy report section of the AMR bank, we consider the Lead-1-AMR model as the baseline.
Question: Which other methods do they compare with?

SOLUTION:
Lead-3 Lead-1-AMR