Dialogue systems in the open domain have achieved great success due to large conversation data and the development of deep learning, but multi-turn systems are often restricted with the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration since it has brought general improvement over multi-turn dialogue systems in different domains. In the task , we propose a novel semi autoregressive generator (SARG) with the high efficiency and flexibility , which is inspired by the autoregression for generation and the sequence labeling for overlapped rewriting . Moreover, experiments on Restoration-200k show that our proposed model significantly outperforms the state-of-the-art models with faster inference speed.
Dialogue systems in the open domain have achieved great success due to large conversation data and the development of deep learning, but multi-turn scenarios are still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration since it has brought general improvement over multi-turn dialogue systems in recent studies. Inspired by the autoregression for 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 with faster inference speed.
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systems are often restricted with
scenarios are still a challenge because of
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different domains. In the task
recent studies. Inspired by the autoregression for generation and the sequence labeling for text editing
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, which is inspired by the autoregression for generation and the sequence labeling for overlapped rewriting
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Restoration-200k
Restoration-200k