| 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. |
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Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning (C18-1)
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| Challenge: | Existing methods for implicit discourse relation recognition ignore bidirectional interactions between two arguments and sparsity of pair patterns. |
| Approach: | They propose a neural Tensor network framework with interactive attention and sparse learning for implicit discourse relation recognition. |
| Outcome: | The proposed framework is effective on PDTB and can be used in text summarization, conversation system and so on. |
Deep Enhanced Representation for Implicit Discourse Relation Recognition (C18-1)
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| 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. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
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| 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. |
Enhancing Reasoning Capabilities by Instruction Learning and Chain-of-Thoughts for Implicit Discourse Relation Recognition (2023.findings-emnlp)
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| 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. |
Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
| Outcome: | The proposed model achieves state-of-the-art on the PDTB 3.0 corpus. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
Using active learning to expand training data for implicit discourse relation recognition (D18-1)
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| 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. |
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
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| 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. |
Improving Implicit Discourse Relation Recognition with Semantics Confrontation (2024.lrec-main)
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| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) are unsatisfactory for the task. |
| Approach: | They propose a method that trains PLMs through two semantics enhancers to implicitly differentiate logical and general semantics. |
| Outcome: | The proposed method exceeds baseline by 3.81% F1 score on PDTB 2.0 dataset . it infers discourse logical relations without explicit connectives, but performance remains unsatisfactory . |
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (2020.acl-main)
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| 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. |