Papers with PDTB 2.0
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. |
| Outcome: | The proposed method achieves outstanding performance against the current state-of-the-art models. |
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. |
What Causes the Failure of Explicit to Implicit Discourse Relation Recognition? (2024.naacl-long)
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| Challenge: | Prior work claimed that explicit classifiers perform poorly in implicit scenarios . a label shift occurs after connectives are removed, but no empirical evidence supports this claim . |
| Approach: | They propose to prove that the discourse relations expressed by some explicit instances will change when connectives disappear. |
| Outcome: | The proposed methods outperform strong baselines on PDTB 2.0, PDTT 3.0, and the GUM dataset. |
Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition (2023.acl-long)
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| Challenge: | Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments. |
| Approach: | They propose a prompt-based multi-level implicit discourse relation recognition framework that leverages parameter-efficient prompt tuning to drive inputted arguments to match the pre-trained space. |
| Outcome: | The proposed framework achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines. |
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition (2023.findings-acl)
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| Challenge: | Existing methods to integrate whole hierarchical information of senses into discourse relation representations for multi-level sense recognition ignore static hierarchic structure containing all senses and ignore hierarchically sense label sequence corresponding to each instance. |
| Approach: | They propose to use a GlObal and Local Hierarchy-aware Contrastive Framework to model two kinds of hierarchies with the aid of multi-task learning and contrastive learning to learn better representations of discourse relation relationships. |
| Outcome: | The proposed method outperforms current state-of-the-art models at all hierarchical levels on PDTB 2.0 and PDTP 3.0 datasets. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
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| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
| Approach: | They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives. |
| Outcome: | The proposed model outperforms baselines on three datasets and is highly accurate. |