We select feedback of varying grade by directly inspecting the optimal w * , thus this feedback is idealized.
the experiment also has a realistic background since we show that α-informative feedback corresponds to improvements under standard evaluation metrics such as lowercased and tokenized TER, and that learning from weak and strong feedback leads to convergence in TER on test data.