A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification (C18-1)
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| Challenge: | Existing studies on implicit discourse relation classification have shown success using feedforward networks and convolutional neural networks. |
| Approach: | They propose to augment input text with external knowledge and context and adopt a neural network model that can effectively handle the augmented text. |
| Outcome: | The proposed model outperforms existing models on implicit discourse relation classification. |
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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. |
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| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
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| Challenge: | Existing approaches to implicit discourse relation recognition reach F1 scores of 9.95% to 37.67% . a neural network exploits the strong correlation between pairs of words that implicitly signal a discourse relation. |
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Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning (C18-1)
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| Challenge: | Existing methods for implicit discourse relation recognition ignore bidirectional interactions between two arguments and sparsity of pair patterns. |
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Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives (2020.lrec-1)
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| Challenge: | Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted. |
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Improving Implicit Discourse Relation Recognition with Semantics Confrontation (2024.lrec-main)
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| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) are unsatisfactory for the task. |
| Approach: | They propose a method that trains PLMs through two semantics enhancers to implicitly differentiate logical and general semantics. |
| Outcome: | The proposed method exceeds baseline by 3.81% F1 score on PDTB 2.0 dataset . it infers discourse logical relations without explicit connectives, but performance remains unsatisfactory . |
Semi-Supervised Tri-Training for Explicit Discourse Argument Expansion (2020.lrec-1)
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| Challenge: | a novel application of semi-supervision for shallow discourse parsing is described . we focus on explicit discourse arguments, but we leave the sense selection aside . |
| Approach: | They propose a semi-supervised approach for shallow discourse parsing using sequence tagging. |
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