Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Table 2: Token accuracy and official F1 for NER.
['Method', 'Dev. Results Acc', 'Dev. Results F1', 'Test Results Acc', 'Test Results F1']
We observed that the RAML model obtained the best results at τ=0.2 for NER, and τ=0.3 for dependency parsing. Beyond τ=0.4, RAML models get worse than the ML baseline for both the two tasks, showing that in practice selection of temperature τ is needed. In addition, the rewards we directly optimized in training (token-level accuracy for NER and UAS for dependency parsing) are more stable w.r.t. τ than the evaluation metrics (F1 in NER), illustrating that in practice, choosing a training reward that correlates well with the evaluation metric is important.