What Causes the Failure of Explicit to Implicit Discourse Relation Recognition? (2024.naacl-long)
Copied to clipboard
| 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. |
Similar Papers
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
Copied to clipboard
| Challenge: | Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature. |
| Approach: | They propose an improved evaluation protocol for implicit relation classification on PDTB 2.0 . they report strong baseline results from pretrained sentence encoders . |
| Outcome: | The proposed evaluation protocol improves the existing framework and provides strong baseline results. |
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. |
Clarifying Underspecified Discourse Relations in Instructional Texts (2025.findings-acl)
Copied to clipboard
| Challenge: | Discourse relations can be optionally realized through explicit connectives such as “but” and “while”. |
| Approach: | They build a corpus of 4,274 text revisions in which a connective was explicitly inserted . they collect plausibility annotations on other connectives to check whether they represent suitable alternatives . |
| Outcome: | The proposed model predicts plausibility of individual connectives with up to 66% accuracy, but is not reliable when multiple relations are plausible. |
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted. |
| Approach: | They propose to apply BERT to implicit discourse relation classification by performing additional pre-training on text tailored to discourse relations. |
| Outcome: | The proposed methods outperform previous state-of-the-art models in many tasks. |
Towards Identifying Alternative-Lexicalization Signals of Discourse Relations (2022.coling-1)
Copied to clipboard
| Challenge: | Existing shallow discourse parsing methods have been limited to identifying relations signaled by a discourse connective and those without a signal. |
| Approach: | They propose to identify relations signalled by a discourse connective and those without . they compare a pattern-based approach and a sequence labeling model . |
| Outcome: | The proposed approach is based on a pattern-based approach and a sequence labeling model. |
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB. |
Is Partial Linguistic Information Sufficient for Discourse Connective Disambiguation? A Case Study of Concession (2025.acl-srw)
Copied to clipboard
| Challenge: | Discourse relations are often not linguistically marked, but there are various connectives that explicitly signal discourse relations. |
| Approach: | They analyze linguistic features that play an important role in disambiguation of polysemous connectives in Japanese by performing a neural language model. |
| Outcome: | The proposed model performed well after removal of one of the two arguments that constitute the discourse relation, but significantly degraded disambiguation performance. |
Using active learning to expand training data for implicit discourse relation recognition (D18-1)
Copied to clipboard
| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
| Approach: | They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives. |
| Outcome: | The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. |
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. |
Multi-Label Classification for Implicit Discourse Relation Recognition (2024.findings-acl)
Copied to clipboard
| Challenge: | Prior research in discourse relation recognition has treated these instances as separate examples during training, with a gold-standard prediction matching one of the labels considered correct at test time. |
| Approach: | They propose to use multiple labels to annotate an example when multiple relations are believed to hold simultaneously. |
| Outcome: | The proposed frameworks don't depress performance for single-label prediction. |