Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots
Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns).
['[BOLD] Interlocutor’s', '[BOLD] Persona Model', '[BOLD] Persona Model', '[BOLD] ICRED (ours)', '[BOLD] ICRED (ours)']
Comparative Results. We can clearly see that the persona model has a sparsity issue: it performs very poorly on sparse learning data (e.g., BLEU score = 8.47 on “[0, 100]”) while it achieves good performances on plentiful learning data (e.g., BLEU score = 9.51 on “(5000, +∞)”), which demonstrates that the fixed person vectors in the persona model need to be learned from large-scale training data for each interlocutor. In contrast, ICRED exploits interactive interlocutor representation learned from current dialog context rather than the fixed person vectors obtained from all training dialog utterances. Therefore, ICRED has no sparsity issues and it performs closely on sparse and plentiful learning data.