Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)
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| Challenge: | Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences. |
| Approach: | They propose a graph-based Context Tracking Network to model the discourse context for IDRR. |
| Outcome: | The proposed model can integrate sentence-level and token-level contextual semantics better than existing models. |
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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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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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Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods to aid implicit discourse relation recognition (IDRR) lack explicit connectives and are difficult to implement on fine-grained IDRR. |
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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. |
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| Challenge: | Existing works on implicit discourse relation recognition focus on syntax features and lack of connectives. |
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Using Subtext to Enhance Generative IDRR (2025.acl-short)
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| Challenge: | Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT. |
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Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)
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| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) lack connectives, which is a major challenge in discourse analysis research. |
| Approach: | They propose a method to predict latent correlations between connectives and discourse relations using a knowledge distillation approach. |
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Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition (2023.emnlp-main)
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| Challenge: | Existing methods for identifying discourse relations without explicit connectives are limited by the availability of annotated data. |
| Approach: | They propose a method that injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. |
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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. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
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| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
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