Challenge: Identifying implicit discourse relations in written text is challenging, but it is also crucial to understand them in spoken discourse.
Approach: They propose a method for implicit discourse relation identification that uses audio and text data to extract semantically equivalent pairs of implicit and explicit discourse markers.
Outcome: The proposed method outperforms audio-based models but can be augmented by combining text and audio features.

Similar Papers

Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)

Copied to clipboard

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.
Outcome: The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features.
Deep Enhanced Representation for Implicit Discourse Relation Recognition (C18-1)

Copied to clipboard

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.
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)

Copied to clipboard

Challenge: despite recent advances in speech processing, the majority of world languages and dialects remain uncovered.
Approach: They propose to collect and transcribe a new Arabic dataset for eight dialects . they also develop strong baselines exploiting the new dataset .
Outcome: The proposed dataset covers eight Arabic dialects, including Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni.
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.
Attention for Implicit Discourse Relation Recognition (L18-1)

Copied to clipboard

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 .
Outcome: The proposed model outperforms state-of-the-art models on fine-grained classification and fine-granular classification while computing parameters without pooling and fully connected layers.
WojoodRelations: Arabic Relation Extraction Corpus and Modeling (2025.emnlp-main)

Copied to clipboard

Challenge: Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets.
Approach: They propose to use WojoodRelations to extract relation relationships from Arabic textual data using relation-aware templates and GPT-Joint to perform relation-based retrieval.
Outcome: The proposed method achieves a Cohen’s of 0.92, indicating high reliability, and supervised models achieve 92.89% F1 for RE, while LLMs obtain 72.73% F1 .
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.
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.
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.
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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations