Thus, we model the dependencies between the output variables by learning (globally normalized) node and edge factor functions that jointly optimize a global performance criterion.
we represent the cQA setting as a large undirected graph G=( As shown in Figure 2b, the graph contains six subgraphs: are associated with the three subtasks, while the bipartite subgraphs We associate each node u ∈ V t with an input vector x u , representing the embedding for subtask t, and an output variable y u , representing the class label for subtask t. Similarly, each edge (u, v) ∈ E st is associated with an input feature vector µ(x u , x v ), derived from the node-level features, and an output variable y uv ∈ {1, 2, • • • , L}, representing the state transitions for the pair of nodes.