Enhancing Reasoning Capabilities by Instruction Learning and Chain-of-Thoughts for Implicit Discourse Relation Recognition (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for implicit discourse relation recognition are based on generative models, but some studies suggest they do not perform as well as generic encoder-only models for NLU tasks. |
| Approach: | They propose a classification method that is solely based on generative models and utilize Chain-of-Thoughts to partition the inference process into a sequence of three successive stages. |
| Outcome: | The proposed model outperforms existing models on a natural language understanding task. |
Similar Papers
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. |
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. |
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to implicit discourse relation recognition lack connectives as strong linguistic clues. |
| Approach: | They propose a transS-driven joint learning architecture to translate discourse relations in low-dimensional embedding space and exploit the semantic features of arguments to assist discourse understanding. |
| Outcome: | The proposed model outperforms existing systems on the Penn Discourse TreeBank. |
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. |
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. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
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. |
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. |
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. |
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. |