论文标题
端到端的神经话语Deixis在对话中的解决
End-to-End Neural Discourse Deixis Resolution in Dialogue
论文作者
论文摘要
我们将Lee等人（2018）基于跨度的实体核心模型适应对话中端到端话语解决方案的任务，特别是通过提出对其模型的扩展来利用特定于任务的特征。最终的模型DD-UTT在Codi-Crac 2021共享任务中的四个数据集上实现了最先进的结果。
We adapt Lee et al.'s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.
