Discriminative explicit instance selection for implicit discourse relation classification

doi:10.1007/s11704-023-3058-2

Front. Comput. Sci.

2024, Vol. 18

Issue (4) : 184340 https://doi.org/10.1007/s11704-023-3058-2

Artificial Intelligence

Discriminative explicit instance selection for implicit discourse relation classification

Wei SONG¹(

), Hongfei HAN¹, Xu HAN¹, Miaomiao CHENG¹, Jiefu GONG², Shijin WANG², Ting LIU³

¹. Information Engineering College, Capital Normal University, Beijing 100048, China
². iFLYTEK AI Research Institute, Hefei 230088, China
³. Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin 150006, China

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Abstract

Discourse relation classification is a fundamental task for discourse analysis, which is essential for understanding the structure and connection of texts. Implicit discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training data. In this paper, we propose a discriminative instance selection method to construct synthetic implicit discourse relation data from easy-to-collect explicit discourse relations. An expanded instance consists of an argument pair and its sense label. We introduce the argument pair type classification task, which aims to distinguish between implicit and explicit argument pairs and select the explicit argument pairs that are most similar to natural implicit argument pairs for data expansion. We also propose a simple label-smoothing technique to assign robust sense labels for the selected argument pairs. We evaluate our method on PDTB 2.0 and PDTB 3.0. The results show that our method can consistently improve the performance of the baseline model, and achieve competitive results with the state-of-the-art models.

Keywords discourse analysis PDTB discourse relation implicit discourse relation classification data expansion

Corresponding Author(s): Wei SONG

Issue Date: 15 December 2023

Cite this article:

Wei SONG,Hongfei HAN,Xu HAN, et al. Discriminative explicit instance selection for implicit discourse relation classification[J]. Front. Comput. Sci., 2024, 18(4): 184340.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-3058-2
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I4/184340

Fig.1 An overview of the proposed method. We train an argument pair type classifier, which gives the probability of being naturally implicit for a given argument pair. Together with a label-smoothing strategy for sense assignment, synthetic data for implicit discourse relation classification is created

Tab.1 Argument pair type classification results

Tab.2 The number of expanded argument pairs for top-level class senses

Tab.3 Hyper-parameter settings for training

Tab.4 Overall comparison results on PDTB 2.0 and PDTB 3.0

Tab.5 Overall results of explicit data expansion methods

Tab.6 Comparison results on four top-level class senses

Tab.7 Results on intra/inter-sentential discourse relations on PDTB 3.0

	Top-level		Second-level
	Macro-F1	ACC	ACC
		PDTB 2.0
Baseline	62.83	69.46	58.96
LA	62.23	68.79	58.83
LA + LS	62.12	69.46	59.09
APTC + LS	64.29	69.75	61.69
APTC + LS $s t d$	62.60	68.79	60.56
		PDTB 3.0
Baseline	68.86	74.41	62.93
LA	68.93	73.83	63.16
LA + LS	67.15	73.01	63.00
APTC + LS	69.94	74.98	64.20
APTC + LS $s t d$	68.72	73.89	63.27

Tab.8 Ablation study of the proposed method

Tab.9 The performance of DeBERTa-large on IDRC

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[1]	Sheng XU, Peifeng LI, Qiaoming ZHU. Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese[J]. Front. Comput. Sci., 2024, 18(3): 183312-.
[2]	Jiaqi LI, Ming LIU, Bing QIN, Ting LIU. A survey of discourse parsing[J]. Front. Comput. Sci., 2022, 16(5): 165329-.
[3]	Yu HONG, Siyuan DING, Yang XU, Xiaoxia JIANG, Yu WANG, Jianmin YAO, Qiaoming ZHU, Guodong ZHOU. Focus-sensitive relation disambiguation for implicit discourse relation detection[J]. Front. Comput. Sci., 2019, 13(6): 1266-1281.

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