Deep Enhanced Representation for Implicit Discourse Relation Recognition (C18-1)
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| Challenge: | Discourse parsing requires understanding of text spans and can't be easily derived from surface features from sentence pairs. |
| Approach: | They propose a model augmented with different grained text representations to improve discourse relation recognition. |
| Outcome: | The proposed model achieves state-of-the-art accuracy with greater than 48% in 11-way and F1 score greater than 50% in 4-way classifications for the first time according to our best knowledge. |
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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. |
| Approach: | They propose a neural network which exploits strong correlation between pairs of words . they use an encoder-decoder model with attention to detect a latent discourse relation . |
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Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
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| Challenge: | Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature. |
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| Challenge: | Existing models for implicit discourse relation recognition are based on generative models, but some studies suggest they do not perform as well as generic encoder-only models for NLU tasks. |
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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. |
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| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
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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. |
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Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
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Using active learning to expand training data for implicit discourse relation recognition (D18-1)
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| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
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Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation (2021.emnlp-main)
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| Challenge: | Existing methods of implicit discourse relation recognition (IDRR) focus on three aspects: enhancing discourse units representation, enhancing semantic interaction, and joint learning with other tasks. |
| Approach: | They propose a joint model to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously. |
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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. |
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