Conversation Modeling on Reddit using a Graph-Structured LSTM
Table 3: Average F1 score of karma level prediction for node-independent (indep) vs. graph-structured (graph) models with and without text features; interp corresponds to an interpolation of the graph-structured model without text and the node-independent model with text; and graph(f) corresponds to a graph-structured model contains forward direction only.
['[BOLD] Model', '[BOLD] Text', '[BOLD] askwomen', '[BOLD] askmen', '[BOLD] politics']
In experiments for all the subreddits, graph-structured models outperform the corresponding node-independent models both with and without language features. Language features also give a greater performance gain when used in the graph-LSTM models. The fact that the forward graph improves over the interpolated models shows that it is not simply the information in the current node that matters for karma of that node. Finally, while the full model outperforms the forward-only version for all the subreddits , the gain is smaller than that obtained by the forward direction alone over the node-independent model, so the forward direction seems to be more important.