Aspect-Controlled Neural Argument Generation
Table 2: In- and cross-topic Recall@k of the ranker used for aspect candidate recommendations.
['[BOLD] Setting', '[BOLD] Rec@5', '[BOLD] Rec@10', '[BOLD] Rec@15', '[BOLD] Rec@20']
A single data sample is represented by an argument and an 1- to 4-gram of this argument, separated by the BERT architecture’s [SEP] token. This technique expands the 800 original samples of the dataset to around 80,336. We use the mean squared error as loss and take the recall@k to compare the models. All results are the average over runs with five different seeds (and over all four splits for the cross-topic experiments).