Improving End-of-Turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task
Table 4: Detecting turn-switches and turn-holds for each speaker intention class, as well as the per-class accuracy for detecting intentions by the auxiliary task.
['[EMPTY]', '[BOLD] Turn-Transitions F1 (switch)', '[BOLD] Turn-Transitions F1 (hold)', '[BOLD] Intentions per-class Acc.']
The results show that the model is better able to predict turn-switches when presented with a backchannel or a question, and is better able to predict turn-holds when presented with a statement, opinion, or an answer. This suggests that the performance of the model depends on the context and nature of a dialogue, and that it is easier to anticipate turn-switches or turn-holds for some intentions and not for others. The table shows that it is easier to identify backchannels, questions, or turn-exit signals (abandon) than it is to identify agreement signals and statements. The auxiliary task obtains an unweighted average recall (UAR) of 45.6% on a 7-way classification task (where chance UAR is 14.3%).