Discourse coherence theories posit relations between text spans as a key feature of coherent texts.However, existing work on coherence modeling has paid little attention to discourse relations.In this paper, we provide empirical evidence to demonstrate that relation features are correlated with text coherence.Then, we investigate a novel fusion model that uses position-aware attention and a visible matrix to combine text-and relation-based features for coherence assessment.Experimental results on two benchmarks show that our approaches can significantly improve baselines, demonstrating the importance of relation features for coherence modeling.

In linguistics, coherence can be achieved by different means, such as by maintaining reference to the same set of entities across sentences and by establishing discourse relations between them.However, most existing work on coherence modeling focuses exclusively on either entity features or discourse relation features, with little attention given to combining the two.In this study, we explore two methods for jointly modeling entities and discourse relations for coherence assessment.Experiments on three benchmark datasets show that integrating both types of features significantly enhances the performance of coherence models, highlighting the benefits of modeling both simultaneously for coherence evaluation.

Wei Liu, Stephen Wan, Michael Strube. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Implicit discourse relation classification is a challenging task due to the absence of discourse connectives.To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB.Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives.To prevent our relation classifier from being misled by poor connectives generated at the early stage of training while alleviating the discrepancy between training and inference, we adopt Scheduled Sampling to the joint learning.We evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC.Results show that our joint model significantly outperforms various baselines on three datasets, demonstrating its superiority for the task.

Coherence is an important aspect of text quality, and various approaches have been applied to coherence modeling.However, existing methods solely focus on a single document's coherence patterns, ignoring the underlying correlation between documents.We investigate a GCN-based coherence model that is capable of capturing structural similarities between documents.Our model first creates a graph structure for each document, from where we mine different subgraph patterns.We then construct a heterogeneous graph for the training corpus, connecting documents based on their shared subgraphs.Finally, a GCN is applied to the heterogeneous graph to model the connectivity relationships.We evaluate our method on two tasks, assessing discourse coherence and automated essay scoring.Results show that our GCN-based model outperforms all baselines, achieving a new state-of-the-art on both tasks.

Wei Liu, Xiyan Fu, Yue Zhang, Wenming Xiao. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.