Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the integration of a latent graph for CSRL. We propose to automatically induce a predicate-oriented latent graph (POLar) with a predicate-centered Gaussian mechanism, by which the nearer and informative words to the predicate will be allocated with more attention. The POLar structure is then dynamically pruned and refined so as to best fit the task need. We additionally introduce an effective dialogue-level pre-trained language model, CoDiaBERT, for better supporting multiple utterance sentences and handling the speaker coreference issue in CSRL. Our system outperforms best-performing baselines on three benchmark CSRL datasets with big margins, especially achieving over 4% F1 score improvements on the cross-utterance argument detection. Further analyses are presented to better understand the effectiveness of our proposed methods.
翻译:语音作用标签( CSRL) 是一项新提议的任务, 揭示了对话文本中的浅语义结构。 不幸的是, CSRL 任务的若干重要特征被现有工作忽略了, 例如结构信息整合、 近邻影响等。 在这项工作中, 我们调查CSRL 的潜在图形的整合情况。 我们提议自动引入一个以上游为主的Gaussian 机制(POLarar), 通过这个机制, 将更多关注定位于上游的更近、更丰富的文字。 然后, POLarar 结构被动态地调整和完善, 以便最好地满足任务需要。 我们还引入了有效的对话级预先培训的语言模式 CoDiaBERT, 以更好地支持多语句并处理CSRL 中的演讲者关联问题。 我们的系统比3个基准 CSRL 数据基底值高, 特别是达到4%以上的F1 分数, 以便更好地了解我们拟议方法的有效性。