SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration
Table 4: Ablation study of proposed model on the test set. The beam size is fixed to 1.
['[EMPTY]', 'f_{1}', 'f_{2}', 'f_{3}', 'BLEU-1', 'BLEU-2', 'ROUGE-1', 'ROUGE-2']
In this subsection, we analyze the design choices crucial for the good performance of our proposed SARG, which includes pretrained weights (WEIGHT), copy mechanism (COPY), and generation from vocabulary (GEN). By contrast, the absence of COPY or WEIGHT may raise a substantial lack of performance. Without the COPY, the model only selects words from pre-defined vocabulary, and the decoder is more difficult to be trained well. Without the WEIGHT, the model needs to be optimized from scratch.