Papers with implicit discourse relation classification
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)
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| Challenge: | Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction. |
| Approach: | They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence. |
| Outcome: | The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB. |
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain (2021.acl-short)
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| Challenge: | Discourse relation classification is a challenging task when the text domain is different from the standard Penn Discourse Treebank (PDTB) training corpus domain. |
| Approach: | They propose to use the Biomedical Discourse Relation Bank to improve discourse relational argument representation by linking explicit instances of similar relations with a voting pipeline. |
| Outcome: | The proposed model outperforms the pre-trained BioBERT model by 2% points. |
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification (2020.coling-main)
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| Challenge: | inessential words are unintentionally misjudged as attention-worthy words and assigned heavier attention weights than should be. |
| Approach: | They propose a penalty-based method to regulate the attention learning process by integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. |
| Outcome: | The proposed method improves on the Penn Discourse TreeBank corpus and is competitive compared to the state-of-the-art methods. |
Implicit Discourse Relation Classification For Nigerian Pidgin (2025.coling-main)
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| Challenge: | Existing discourse parsing tools are not available for Nigerian Pidgin (NP) this task requires supervised training and requires prompting. |
| Approach: | They propose to use implicit discourse relation classification (IDRC) for Nigerian Pidgin, which requires supervised training. |
| Outcome: | The proposed framework outperforms baseline and NP IDR classifiers in f1 scores. |
Multimodal Extraction and Recognition of Arabic Implicit Discourse Relations (2025.coling-main)
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| Challenge: | Identifying implicit discourse relations in written text is challenging, but it is also crucial to understand them in spoken discourse. |
| Approach: | They propose a method for implicit discourse relation identification that uses audio and text data to extract semantically equivalent pairs of implicit and explicit discourse markers. |
| Outcome: | The proposed method outperforms audio-based models but can be augmented by combining text and audio features. |