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

No. 14: AAAI-21 Technical Tracks 14

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Speech and Natural Language Processing I

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, 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 Restoration-200k show that our proposed model significantly outperforms the state-of-the-art models in terms of quality and inference speed.

10.1609/aaai.v35i14.17543