To predict correct TDM triples and associate the appropriate scores, we adopt a natural language inference approach (NLI) (Poliak et al., 2018) and learn a binary classifier for pairs of document contexts and TDM label hypotheses.
we split the problem into two tasks: (1) given a document representation DocTAET, we would like to predict whether a specific TDM triple can be inferred (e.g., give a document we infer <Summarization, Gigaword, ROUGE-2>); (2) we predict whether a <Dataset, Metric> tuple (DM) can be inferred given a score context SC.