Improving End-of-Turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task
Table 3: Performance comparison of different methods. Results shown are macro-averages across turn-switches and turn-holds.
['[BOLD] Method', 'Rec.', 'Prec.', 'F1', 'AUC']
We attribute this improvements to better feature representations and better sequential modeling abilities of LSTMs. The table shows that a multi-task LSTM, which is trained using a joint loss function, provides consistent improvements over a single-task LSTM (significant improvements under a paired t-test, p<0.05, in terms of recall and AUC). This suggests that a turn prediction model can benefit from representations extracted for detecting speaker intentions. Next, we study how well our model is able to identify turn-switches when the switches are smooth and when they are overlapping. Our model identifies turn-switches with a recall of 68.5% when the switches are overlapping and identifies turn-switches with a recall of 68.1% when the switches are smooth.