Robust Non-Explicit Neural Discourse Parser in English and Chinese

Rutherford, Attapol T.; Xue, Nianwen

doi:10.18653/v1/k16-2007

Cited by 25 publications

(7 citation statements)

References 9 publications

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“…The experiments are conducted on two datasets, the PDTB 2.0 (Prasad et al, 2008) and the CoNLL-2016 shared task (CoNLL16) (Xue et al, 2016), to validate the performance of our method. Both contain the Wall Street Journal (WSJ) articles, and the difference is the annotation and relation senses.…”

Section: Datasetmentioning

confidence: 99%

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Chan¹,

Liu²,

Cheng³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives. The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure. Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification. We argue that it is more effective to predict the paths inside the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than flat labels (e.g., Contrast) or connectives (e.g., however). We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR. This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.

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Section: Datasetmentioning

confidence: 99%

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Chan¹,

Liu²,

Cheng³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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show abstract

“…Most state-of-the-art research in discourse analysis specifically has focused on classifying the discourse relations between pairs of clauses, as is practice in the Penn Discourse Treebank (PDTB) (Prasad et al, 2008) and Rhetorical Structure Theory (RST) dataset (Carlson et al, 2003). Corpora and methods have been developed to predict explicit discourse connectives (Miltsakaki et al, 2004;Lin et al, 2009;Das et al, 2018;Malmi et al, 2018;Wang et al, 2018) as well as implicit discourse relations (Rutherford and Xue, 2016;Liu et al, 2016;Lan et al, 2017;Lei et al, 2017). Choubey et al ( 2020) built a news article corpus where each sentence was labeled with a discourse label defined in Van Dijk schema (Van Dijk, 2013).…”

Section: Related Workmentioning

confidence: 99%

Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification

Spangher¹,

May²,

Shiang³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small classimbalanced datasets. In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. We show an improvement of 4.9% Micro F1-score over current state-of-the-art benchmarks on the NewsDiscourse dataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes. We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting 1 .

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“…Most state-of-the-art research in discourse analysis has focused on classifying the discourse relations between pairs of clauses, as is practice in the Penn Discourse Treebank (PDTB) (Prasad et al, 2008) and Rhetorical Structure Theory (RST) dataset (Carlson et al, 2003). Corpora and methods have been developed to predict explicit discourse connectives (Miltsakaki et al, 2004;Lin et al, 2009;Das et al, 2018;Malmi et al, 2017;Wang et al, 2018) as well as implicit discourse re-lations (Rutherford and Xue, 2016;Liu et al, 2016;Lan et al, 2017;Lei et al, 2017). Choubey et al ( 2020) built a news article corpus where each sentence was tagged with a discourse label defined in Van Dijk schema (Van Dijk, 2013).…”

Section: Related Workmentioning

confidence: 99%

Multitask Learning for Class-Imbalanced Discourse Classification

Spangher,

May,

Shiang

et al. 2021

Preprint

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Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures. In this work, we perform an extensive analysis on sentencelevel classification approaches for the News Discourse dataset, one of the largest high-level semantic discourse datasets recently published. We show that a multitask approach can improve 7% Micro F1-score upon current stateof-the-art benchmarks, due in part to label corrections across tasks, which improve performance for underrepresented classes. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in such a setting.

show abstract

Robust Non-Explicit Neural Discourse Parser in English and Chinese

Cited by 25 publications

References 9 publications

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification

Multitask Learning for Class-Imbalanced Discourse Classification

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