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arXiv · 2008.01474

SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration

Mengzuo Huang·Feng Li·Wuhe Zou·Weidong Zhang
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Abstract

Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration which has brought general improvement over multi-turn dialogue systems in recent studies. Meanwhile, jointly inspired by the autoregression for text generation and the sequence labeling for text editing, we propose a novel semi autoregressive generator (SARG) with the high efficiency and flexibility. Moreover, experiments on two benchmarks show that our proposed model significantly outperforms the state-of-the-art models in terms of quality and inference speed.

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Mengzuo Huang, Feng Li, Wuhe Zou, Weidong Zhang. 2020-08-04. SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration. https://arxiv.org/abs/2008.01474

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