Learning with Weak Supervision for Email Intent Detection
Table 7. Hydra (enc = BiLSTM) on RI task with fixed amount of clean data (i.e. all the 1800 clean instances) and varying percentage of weak labels.
['% weak labels', '0%', '20%', '40%', '60%', '80%', '100%']
Impact of the amount of weak labels: In order to assess the impact of the amount of available weak labels on the performance of Hydra, we perform another experiment where we fix the task and the amount of clean labels, and vary the amount of weakly labeled instances. We perform this experiment with Hydra and BiLSTM encoder for the RI task.