SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration
Table 5: Human evaluation of the restoration quality and language fluency. Both quality and fluency score adopt a 3-point scale.
['[EMPTY]', 'Quality', 'Fluency']
In the phase of human evaluation, we employ three experienced workers to score the restoration quality and sentence fluency separately on 200 randomly selected samples. However, in the aspect of fluency score, Seq2Seq-Uni achieves the best performance. The reasons are two-fold: one is the Seq2Seq-Uni has the lower restoration score, which means the model tends to make fewer changes to the original utterance; the other is that Seq2Seq-Uni takes a way of complete autoregression, which can complete the causal language modeling well.