Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings

Abstract

It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives. We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition

“…Ablation Study of Multi-task Learning (Table 4). Following Dai and Huang (2018) and Nguyen et al (2019), we utilize the explicit discourse relation recognition (EDRR) and connective prediction (CP) as auxiliary tasks to help implicit discourse relation recognition (IDRR). We conduct ablation experiments of the two auxiliary tasks on 4-way classification (Table 4) to show their impact.…”

“…Multi-Task Training. Following previous works (Dai and Huang, 2018;Nguyen et al, 2019), we apply multi-task learning to improve the performance. The main task is implicit discourse relation recognition (IDRR), while the auxiliary tasks are explicit discourse relation recognition (EDRR) and connective prediction (CP).…”

Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation

“…proposed a knowledge-enhanced attention neural network to introduce external knowledge to enhance the interaction. Besides, a few studies combined IDRR with other tasks for joint learning, e.g., explicit relation recognition (Lan et al, 2017), connective prediction (Bai and Zhao, 2018;Shi and Demberg, 2019), and label embedding learning (Nguyen et al, 2019;.…”

“…2) Bai2019 (Bai et al, 2019): it adds the memorizing mechanism to their previous work (Bai and Zhao, 2018). 3) Nguyen2019 (Nguyen et al, 2019): it uses multi-task learning via label embedding. 4) Guo2020 : it is a knowledge-enhanced attention neural network that enhances the interaction between discourses by introducing external knowledge.…”

“…With the success of representation learning in discourse analysis, most existing methods of IDRR * Corresponding author focus on three aspects: enhancing discourse units representation (Ji and Eisenstein, 2015;Qin et al, 2016;Liu and Li, 2016), enhancing semantic interaction (Guo et al, 2018;Ruan et al, 2020;, and joint learning with other tasks (Bai and Zhao, 2018;Nguyen et al, 2019;. They all regard IDRR as a classification task and lack a deeper understanding of the relation semantics; even recent work (Nguyen et al, 2019; with labeling embedding cannot directly introduce the prior knowledge of the discourse relation semantics into their models.…”