Implicit Sense-labeled Connective Recognition as Text Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for identifying implicit discourse relations are limited by the number of possible categories and sense labels. |
| Approach: | They propose a method for identifying the sense label of an implicit connective between adjacent text spans by using an encoder-decoder model. |
| Outcome: | The proposed method outperforms the conventional classification-based method on a shallow discourse parsing dataset. |
Similar Papers
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation (2021.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model achieves the best performance against several state-of-the-art systems on Chinese and English datasets. |
Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed method outperforms state-of-the-art models on coarse-grained and fine-grain discourse relations and can be transferred to explicit discourse relation recognition and achieve acceptable performance. |
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies on implicit discourse relation recognition have shown great success. |
| Approach: | They incorporate the sense hierarchy into the recognition process and use it to select negative examples. |
| Outcome: | The proposed approach achieves state-of-the-art performance on the task with no additional effort. |
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings (P19-1)
Copied to clipboard
| Challenge: | Existing models for implicit discourse relation recognition lack the ability to accurately map connectives into discourse relations. |
| Approach: | They propose a multi-task learning framework where relations and connectives are simultaneously predicted and leveraged to transfer knowledge between the two prediction tasks. |
| Outcome: | The proposed framework yields state-of-the-art performance on several settings of the Penn Discourse Treebank dataset. |
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to aid implicit discourse relation recognition (IDRR) lack explicit connectives and are difficult to implement on fine-grained IDRR. |
| Approach: | They propose a Prompt-based Connective Prediction method that instructs large-scale pre-trained models to use knowledge relevant to discourse relation and utilizes strong correlation between connectives and discourse relation to help the model recognize implicit discourse relations. |
| Outcome: | The proposed method surpasses the state-of-the-art model and achieves significant improvements on those fine-grained few-shot discourse relation classes. |
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition (2022.coling-1)
Copied to clipboard
| Challenge: | Existing paradigms for Implicit Discourse Relation Recognition (IDRR) do not exploit linguistic evidence embedded in the pre-training process. |
| Approach: | They propose a new paradigm to detect and classify relation sense between two text segments without an explicit connective. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art algorithms even with fewer training data. |
DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing works on implicit discourse relation recognition focus on syntax features and lack of connectives. |
| Approach: | They propose a prompt-based path prediction method that integrates the interactive information and intrinsic senses among the hierarchy in IDRR. |
| Outcome: | The proposed method shows significant improvement against competitive baselines. |
An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to recognize implicit discourse relations are limited by the lack of training data. |
| Approach: | They propose a method to generate synthetic data for IDRR using a large language model . they extract confused discourse relation pairs based on false negative rate and use two-stage prompting to generate effective synthetic data. |
| Outcome: | The proposed method achieves state-of-the-art macro-F1 performance without sacrificing micro-F1. |
Using Subtext to Enhance Generative IDRR (2025.acl-short)
Copied to clipboard
| 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. |
| Approach: | They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama . |
| Outcome: | The proposed approach achieves higher F1 scores on two benchmarks than previous models. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
Copied to clipboard
| 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. |