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70 Publications visible to you, out of a total of 70
Modeling graphs beyond hyperbolic: Graph neural networks in symmetric positive definite matrices

Abstract

Not specified

Authors: Wei Zhao, Federico López, J. Maxwell Riestenberg, Michael Strube, Diaaeldin Taha, Steve Trettel

Date Published: 18th Sep 2023

Publication Type: InProceedings

HITS at DISRPT 2023: Discourse segmentation, connective detection, and relation classification

Abstract

Not specified

Authors: Wei Liu, Yi Fan, Michael Strube

Date Published: 14th Jul 2023

Publication Type: InProceedings

Proceedings of the 4th Workshop on Computational Approaches to Discourse

Abstract

Not specified

Authors: Chloé Braud, Christian Hardmeier, Junyi Jessy Li, Sharid Loaciga, Michael Strube, Amir Zeldes

Date Published: 13th Jul 2023

Publication Type: Proceedings

Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers

Abstract (Expand)

Automating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for non-expert readers in their local language. We introduce CSJ as a downstream … task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. We analyze the performance of possible existing solutions as baselines for the CSJ task. Based on these findings, we propose to combine the three components - SELECT, SIMPLIFY and REWRITE (SSR) to produce cross-lingual simplified science summaries for non-expert readers. Our empirical evaluation on the WIKIPEDIA dataset shows that SSR significantly outperforms the baselines for the CSJ task and can serve as a strong baseline for future work. We also perform an ablation study investigating the impact of individual components of SSR. Further, we analyze the performance of SSR on a high-quality, real-world CSJ dataset with human evaluation and in-depth analysis, demonstrating the superior performance of SSR for CSJ.

Authors: Mehwish Fatima, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks

Abstract (Expand)

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 identifies the graph structure of each document, from where we mine different sub-graph 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 baselines, achieving a new state-of-the-art on both tasks.

Authors: Wei Liu, Xiyan Fu, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation

Abstract (Expand)

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.

Authors: Wei Liu, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

DiscoScore: Evaluating text generation with BERT and discourse coherence

Abstract (Expand)

Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based … evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation systems. In this work, we introduce DiscoScore, a parametrized discourse metric, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory. Our experiments encompass 16 non-discourse and discourse metrics, including DiscoScore and popular coherence models, evaluated on summarization and document-level machine translation (MT). We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level—which is particularly problematic as systems are typically compared in this manner. DiscoScore, in contrast, achieves strong system-level correlation with human ratings, not only in coherence but also in factual consistency and other aspects, and surpasses BARTScore by over 10 correlation points on average. Further, aiming to understand DiscoScore, we provide justifications to the importance of discourse coherence for evaluation metrics, and explain the superiority of one variant over another. Our code is available at https://github.com/AIPHES/DiscoScore.

Authors: Wei Zhao, Michael Strube, Steffen Eger

Date Published: 2nd May 2023

Publication Type: InProceedings

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