论文标题
与面向谓词的潜在图的对话语义角色标记
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph
论文作者
论文摘要
会话语义角色标签（CSRL）是一项新提出的任务，在对话文本中揭示了浅层语义结构。不幸的是，现有作品（例如结构信息整合，近邻居的影响力）忽略了CSRL任务的几个重要特征。在这项工作中，我们研究了CSRL潜在图的集成。我们建议以一种以谓词为中心的高斯机制自动诱导面向谓词的潜在图（极性），通过该机制，将对谓词的更接近和信息含量的单词进行分配，并更多地关注。然后，将极性结构动态修剪和完善，从而最适合任务需求。我们还介绍了有效的对话级预训练的语言模型Codiabert，以更好地支持多个话语句子并处理CSRL中的说话者核心问题。我们的系统在三个基准CSRL数据集上的表现优于最佳表现基线，尤其是在跨块剂量参数检测中实现了超过4％的F1得分提高。提出了进一步的分析，以更好地了解我们提出的方法的有效性。
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.
