将不完整话语重写视为序列贪心标注
Incomplete Utterance Rewriting as Sequential Greedy Tagging
AI总结：
针对不完整话语重写中上下文信息提取困难的问题，提出融合说话人感知嵌入的序列贪心标注模型，在九项恢复指标上取得最优结果，并提升推理速度。
摘要：
不完整话语重写任务近来受到广泛关注。以往模型难以从对话上下文中提取信息，较低的恢复分数即证明了这一点。为解决该问题，我们提出一种基于序列标注的新型模型，它更擅长从上下文中提取信息。同时，我们引入说话人感知嵌入来建模说话人变化。在多个公开数据集上的实验表明，我们的模型在全部九项恢复分数上均取得最优结果，同时其他指标分数可与以往最先进模型相媲美。此外，得益于模型的简洁性，我们的方法在推理速度上优于大多数以往模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
