论文标题
SARG：一种新型的半自动回旋发电机，用于多转的不完整话语修复
SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration
论文作者
论文摘要
由于易于获得的单转弯语料库和深度学习的发展，开放型领域中的对话系统取得了巨大的成功，但是由于频繁的核心和信息遗漏，多转变的情况仍然是一个挑战。在本文中，我们调查了不完整的话语恢复，这在最近的研究中带来了对多转向对话系统的总体改进。同时，我们共同启发了文本生成的自动性和文本编辑的序列标签，我们提出了一种具有高效率和灵活性的新型半自动回归发生器（SARG）。此外，对两个基准测试的实验表明，我们提出的模型在质量和推理速度方面显着优于最先进的模型。
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.
