Incomplete Utterance Rewriting as Sequential Greedy Tagging. (arXiv:2307.06337v1 [cs.LG])

The task of incomplete utterance rewriting has recently gotten much
attention. Previous models struggled to extract information from the dialogue
context, as evidenced by the low restoration scores. To address this issue, we
propose a novel sequence tagging-based model, which is more adept at extracting
information from context. Meanwhile, we introduce speaker-aware embedding to
model speaker variation. Experiments on multiple public datasets show that our
model achieves optimal results on all nine restoration scores while having
other metric scores comparable to previous state-of-the-art models.
Furthermore, benefitting from the model’s simplicity, our approach outperforms
most previous models on inference speed.



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